no code implementations • EMNLP 2021 • Jiaxin Huang, Chunyuan Li, Krishan Subudhi, Damien Jose, Shobana Balakrishnan, Weizhu Chen, Baolin Peng, Jianfeng Gao, Jiawei Han
This paper presents an empirical study to efficiently build named entity recognition (NER) systems when a small amount of in-domain labeled data is available.
no code implementations • Findings (EMNLP) 2021 • Kezhen Chen, Qiuyuan Huang, Daniel McDuff, Xiang Gao, Hamid Palangi, JianFeng Wang, Kenneth Forbus, Jianfeng Gao
Based on these annotations, we define two different tasks for the NICE dataset.
1 code implementation • ICML 2020 • Hangbo Bao, Li Dong, Furu Wei, Wenhui Wang, Nan Yang, Xiaodong Liu, Yu Wang, Jianfeng Gao, Songhao Piao, Ming Zhou, Hsiao-Wuen Hon
We propose to pre-train a unified language model for both autoencoding and partially autoregressive language modeling tasks using a novel training procedure, referred to as a pseudo-masked language model (PMLM).
1 code implementation • 25 Apr 2024 • An Yan, Zhengyuan Yang, Junda Wu, Wanrong Zhu, Jianwei Yang, Linjie Li, Kevin Lin, JianFeng Wang, Julian McAuley, Jianfeng Gao, Lijuan Wang
Set-of-Mark (SoM) Prompting unleashes the visual grounding capability of GPT-4V, by enabling the model to associate visual objects with tags inserted on the image.
Ranked #47 on Visual Question Answering on MM-Vet
no code implementations • 12 Mar 2024 • Juan Manuel Zambrano Chaves, Shih-Cheng Huang, Yanbo Xu, Hanwen Xu, Naoto Usuyama, Sheng Zhang, Fei Wang, Yujia Xie, Mahmoud Khademi, ZiYi Yang, Hany Awadalla, Julia Gong, Houdong Hu, Jianwei Yang, Chunyuan Li, Jianfeng Gao, Yu Gu, Cliff Wong, Mu Wei, Tristan Naumann, Muhao Chen, Matthew P. Lungren, Serena Yeung-Levy, Curtis P. Langlotz, Sheng Wang, Hoifung Poon
Frontier models such as GPT-4V still have major competency gaps in multimodal capabilities for biomedical applications.
no code implementations • 7 Mar 2024 • Hitesh Kandala, Jianfeng Gao, Jianwei Yang
We present Pix2Gif, a motion-guided diffusion model for image-to-GIF (video) generation.
1 code implementation • 1 Mar 2024 • Zelalem Gero, Chandan Singh, Yiqing Xie, Sheng Zhang, Tristan Naumann, Jianfeng Gao, Hoifung Poon
Summarizing clinical text is crucial in health decision-support and clinical research.
no code implementations • 28 Feb 2024 • Qiuyuan Huang, Naoki Wake, Bidipta Sarkar, Zane Durante, Ran Gong, Rohan Taori, Yusuke Noda, Demetri Terzopoulos, Noboru Kuno, Ade Famoti, Ashley Llorens, John Langford, Hoi Vo, Li Fei-Fei, Katsu Ikeuchi, Jianfeng Gao
Recent advancements in large foundation models have remarkably enhanced our understanding of sensory information in open-world environments.
1 code implementation • 27 Feb 2024 • Yixin Liu, Kai Zhang, Yuan Li, Zhiling Yan, Chujie Gao, Ruoxi Chen, Zhengqing Yuan, Yue Huang, Hanchi Sun, Jianfeng Gao, Lifang He, Lichao Sun
Sora is a text-to-video generative AI model, released by OpenAI in February 2024.
no code implementations • 9 Feb 2024 • Shervin Minaee, Tomas Mikolov, Narjes Nikzad, Meysam Chenaghlu, Richard Socher, Xavier Amatriain, Jianfeng Gao
Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks, since the release of ChatGPT in November 2022.
no code implementations • 8 Feb 2024 • Zane Durante, Bidipta Sarkar, Ran Gong, Rohan Taori, Yusuke Noda, Paul Tang, Ehsan Adeli, Shrinidhi Kowshika Lakshmikanth, Kevin Schulman, Arnold Milstein, Demetri Terzopoulos, Ade Famoti, Noboru Kuno, Ashley Llorens, Hoi Vo, Katsu Ikeuchi, Li Fei-Fei, Jianfeng Gao, Naoki Wake, Qiuyuan Huang
We propose an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents across a wide range of domains, datasets, and tasks.
1 code implementation • 6 Feb 2024 • Yufan Zhuang, Liyuan Liu, Chandan Singh, Jingbo Shang, Jianfeng Gao
We then train MetaTree to produce the trees that achieve strong generalization performance.
no code implementations • 6 Feb 2024 • Tarun Gupta, Wenbo Gong, Chao Ma, Nick Pawlowski, Agrin Hilmkil, Meyer Scetbon, Marc Rigter, Ade Famoti, Ashley Juan Llorens, Jianfeng Gao, Stefan Bauer, Danica Kragic, Bernhard Schölkopf, Cheng Zhang
The study of causality lends itself to the construction of veridical world models, which are crucial for accurately predicting the outcomes of possible interactions.
1 code implementation • 30 Jan 2024 • Chandan Singh, Jeevana Priya Inala, Michel Galley, Rich Caruana, Jianfeng Gao
We highlight two emerging research priorities for LLM interpretation: using LLMs to directly analyze new datasets and to generate interactive explanations.
1 code implementation • 25 Jan 2024 • Yanda Chen, Chandan Singh, Xiaodong Liu, Simiao Zuo, Bin Yu, He He, Jianfeng Gao
We propose explanation-consistency finetuning (EC-finetuning), a method that adapts LLMs to generate more consistent natural-language explanations on related examples.
1 code implementation • 10 Jan 2024 • Lichao Sun, Yue Huang, Haoran Wang, Siyuan Wu, Qihui Zhang, Yuan Li, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bertie Vidgen, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chunyuan Li, Eric Xing, Furong Huang, Hao liu, Heng Ji, Hongyi Wang, huan zhang, Huaxiu Yao, Manolis Kellis, Marinka Zitnik, Meng Jiang, Mohit Bansal, James Zou, Jian Pei, Jian Liu, Jianfeng Gao, Jiawei Han, Jieyu Zhao, Jiliang Tang, Jindong Wang, Joaquin Vanschoren, John Mitchell, Kai Shu, Kaidi Xu, Kai-Wei Chang, Lifang He, Lifu Huang, Michael Backes, Neil Zhenqiang Gong, Philip S. Yu, Pin-Yu Chen, Quanquan Gu, ran Xu, Rex Ying, Shuiwang Ji, Suman Jana, Tianlong Chen, Tianming Liu, Tianyi Zhou, William Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xing Xie, Xun Chen, Xuyu Wang, Yan Liu, Yanfang Ye, Yinzhi Cao, Yong Chen, Yue Zhao
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
1 code implementation • 7 Jan 2024 • Zane Durante, Qiuyuan Huang, Naoki Wake, Ran Gong, Jae Sung Park, Bidipta Sarkar, Rohan Taori, Yusuke Noda, Demetri Terzopoulos, Yejin Choi, Katsushi Ikeuchi, Hoi Vo, Li Fei-Fei, Jianfeng Gao
To accelerate research on agent-based multimodal intelligence, we define "Agent AI" as a class of interactive systems that can perceive visual stimuli, language inputs, and other environmentally-grounded data, and can produce meaningful embodied actions.
2 code implementations • NeurIPS 2023 • Jae Sung Park, Jack Hessel, Khyathi Raghavi Chandu, Paul Pu Liang, Ximing Lu, Peter West, Youngjae Yu, Qiuyuan Huang, Jianfeng Gao, Ali Farhadi, Yejin Choi
Empirical results and human evaluations in a zero-shot setup demonstrate that our distillation method results in more precise VL models of reasoning compared to a baseline of passing a generated referring expression to an LLM.
1 code implementation • 5 Dec 2023 • Hao Zhang, Hongyang Li, Feng Li, Tianhe Ren, Xueyan Zou, Shilong Liu, Shijia Huang, Jianfeng Gao, Lei Zhang, Chunyuan Li, Jianwei Yang
To address this issue, we have created GVC data that allows for the combination of grounding and chat capabilities.
no code implementations • 4 Dec 2023 • Jiarui Xu, Yossi Gandelsman, Amir Bar, Jianwei Yang, Jianfeng Gao, Trevor Darrell, Xiaolong Wang
Given a textual description of a visual task (e. g. "Left: input image, Right: foreground segmentation"), a few input-output visual examples, or both, the model in-context learns to solve it for a new test input.
3 code implementations • 22 Nov 2023 • Feng Li, Qing Jiang, Hao Zhang, Tianhe Ren, Shilong Liu, Xueyan Zou, Huaizhe xu, Hongyang Li, Chunyuan Li, Jianwei Yang, Lei Zhang, Jianfeng Gao
In-context prompting in large language models (LLMs) has become a prevalent approach to improve zero-shot capabilities, but this idea is less explored in the vision domain.
2 code implementations • 13 Nov 2023 • An Yan, Zhengyuan Yang, Wanrong Zhu, Kevin Lin, Linjie Li, JianFeng Wang, Jianwei Yang, Yiwu Zhong, Julian McAuley, Jianfeng Gao, Zicheng Liu, Lijuan Wang
We first benchmark MM-Navigator on our collected iOS screen dataset.
1 code implementation • 9 Nov 2023 • Shilong Liu, Hao Cheng, Haotian Liu, Hao Zhang, Feng Li, Tianhe Ren, Xueyan Zou, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang, Jianfeng Gao, Chunyuan Li
LLaVA-Plus is a general-purpose multimodal assistant that expands the capabilities of large multimodal models.
Ranked #1 on LMM real-life tasks on Leaderboard
1 code implementation • 3 Nov 2023 • Qingru Zhang, Chandan Singh, Liyuan Liu, Xiaodong Liu, Bin Yu, Jianfeng Gao, Tuo Zhao
In human-written articles, we often leverage the subtleties of text style, such as bold and italics, to guide the attention of readers.
2 code implementations • 1 Nov 2023 • Wei-Ge Chen, Irina Spiridonova, Jianwei Yang, Jianfeng Gao, Chunyuan Li
LLaVA-Interactive is a research prototype for multimodal human-AI interaction.
2 code implementations • 21 Oct 2023 • John X. Morris, Chandan Singh, Alexander M. Rush, Jianfeng Gao, Yuntian Deng
Prompting language models (LMs) is the main interface for applying them to new tasks.
1 code implementation • 20 Oct 2023 • Xiao Yu, Baolin Peng, Michel Galley, Jianfeng Gao, Zhou Yu
The self-improving ability of large language models (LLMs), enabled by prompting them to analyze and revise their own outputs, has garnered significant interest in recent research.
no code implementations • 19 Oct 2023 • Xiaodong Yu, Hao Cheng, Xiaodong Liu, Dan Roth, Jianfeng Gao
Finally, we find that the adversarial examples generated by our method are transferable across all considered LLMs.
3 code implementations • 17 Oct 2023 • Jianwei Yang, Hao Zhang, Feng Li, Xueyan Zou, Chunyuan Li, Jianfeng Gao
We present Set-of-Mark (SoM), a new visual prompting method, to unleash the visual grounding abilities of large multimodal models (LMMs), such as GPT-4V.
no code implementations • 16 Oct 2023 • Yu Gu, Jianwei Yang, Naoto Usuyama, Chunyuan Li, Sheng Zhang, Matthew P. Lungren, Jianfeng Gao, Hoifung Poon
In a comprehensive battery of tests on counterfactual medical image generation, BiomedJourney substantially outperforms prior state-of-the-art methods in instruction image editing and medical image generation such as InstructPix2Pix and RoentGen.
no code implementations • 11 Oct 2023 • chengyu dong, Liyuan Liu, Hao Cheng, Jingbo Shang, Jianfeng Gao, Xiaodong Liu
Although ELECTRA offers a significant boost in efficiency, its potential is constrained by the training cost brought by the auxiliary model.
no code implementations • 3 Oct 2023 • Suyu Ge, Yunan Zhang, Liyuan Liu, Minjia Zhang, Jiawei Han, Jianfeng Gao
In this study, we introduce adaptive KV cache compression, a plug-and-play method that reduces the memory footprint of generative inference for Large Language Models (LLMs).
1 code implementation • 3 Oct 2023 • Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai-Wei Chang, Michel Galley, Jianfeng Gao
To bridge this gap, we present MathVista, a benchmark designed to combine challenges from diverse mathematical and visual tasks.
no code implementations • 1 Oct 2023 • Liyuan Liu, Jianfeng Gao, Weizhu Chen
One defining characteristic of Mixture-of-Expert (MoE) models is their capacity for conducting sparse computation via expert routing, leading to remarkable scalability.
1 code implementation • 18 Sep 2023 • Yadong Lu, Chunyuan Li, Haotian Liu, Jianwei Yang, Jianfeng Gao, Yelong Shen
We find that scaling LMM consistently enhances model performance and improves language capabilities, and performance of LoRA/QLoRA tuning of LMM are comparable to the performance of full-model fine-tuning.
Ranked #50 on Visual Question Answering on MM-Vet
1 code implementation • 18 Sep 2023 • Chunyuan Li, Zhe Gan, Zhengyuan Yang, Jianwei Yang, Linjie Li, Lijuan Wang, Jianfeng Gao
This paper presents a comprehensive survey of the taxonomy and evolution of multimodal foundation models that demonstrate vision and vision-language capabilities, focusing on the transition from specialist models to general-purpose assistants.
no code implementations • 18 Sep 2023 • Ran Gong, Qiuyuan Huang, Xiaojian Ma, Hoi Vo, Zane Durante, Yusuke Noda, Zilong Zheng, Song-Chun Zhu, Demetri Terzopoulos, Li Fei-Fei, Jianfeng Gao
Large Language Models (LLMs) have the capacity of performing complex scheduling in a multi-agent system and can coordinate these agents into completing sophisticated tasks that require extensive collaboration.
1 code implementation • 10 Jul 2023 • Feng Li, Hao Zhang, Peize Sun, Xueyan Zou, Shilong Liu, Jianwei Yang, Chunyuan Li, Lei Zhang, Jianfeng Gao
In this paper, we introduce Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity.
1 code implementation • 16 Jun 2023 • Theo X. Olausson, Jeevana Priya Inala, Chenglong Wang, Jianfeng Gao, Armando Solar-Lezama
We hypothesize that this is because self-repair is bottlenecked by the model's ability to provide feedback on its own code; using a stronger model to artificially boost the quality of the feedback, we observe substantially larger performance gains.
no code implementations • NeurIPS 2023 • Weizhi Wang, Li Dong, Hao Cheng, Xiaodong Liu, Xifeng Yan, Jianfeng Gao, Furu Wei
Such a decoupled memory design can easily cache and update long-term past contexts for memory retrieval without suffering from memory staleness.
1 code implementation • 1 Jun 2023 • Paul Soulos, Edward Hu, Kate McCurdy, Yunmo Chen, Roland Fernandez, Paul Smolensky, Jianfeng Gao
To facilitate the learning of these symbolic sequences, we introduce a differentiable tree interpreter that compiles high-level symbolic tree operations into subsymbolic matrix operations on tensors.
no code implementations • NeurIPS 2023 • Chunyuan Li, Cliff Wong, Sheng Zhang, Naoto Usuyama, Haotian Liu, Jianwei Yang, Tristan Naumann, Hoifung Poon, Jianfeng Gao
In this paper, we propose a cost-efficient approach for training a vision-language conversational assistant that can answer open-ended research questions of biomedical images.
1 code implementation • 30 May 2023 • Zelalem Gero, Chandan Singh, Hao Cheng, Tristan Naumann, Michel Galley, Jianfeng Gao, Hoifung Poon
Extracting patient information from unstructured text is a critical task in health decision-support and clinical research.
no code implementations • 24 May 2023 • Miaoran Li, Baolin Peng, Michel Galley, Jianfeng Gao, Zhu Zhang
Fact-checking is an essential task in NLP that is commonly utilized for validating the factual accuracy of claims.
no code implementations • 23 May 2023 • Yu Zhang, Hao Cheng, Zhihong Shen, Xiaodong Liu, Ye-Yi Wang, Jianfeng Gao
Scientific literature understanding tasks have gained significant attention due to their potential to accelerate scientific discovery.
1 code implementation • 21 May 2023 • Linyuan Gong, Chenyan Xiong, Xiaodong Liu, Payal Bajaj, Yiqing Xie, Alvin Cheung, Jianfeng Gao, Xia Song
This paper explores the effectiveness of model-generated signals in improving zero-shot generalization of text-to-text Transformers such as T5.
2 code implementations • 17 May 2023 • Chandan Singh, Aliyah R. Hsu, Richard Antonello, Shailee Jain, Alexander G. Huth, Bin Yu, Jianfeng Gao
Here, we ask whether we can automatically obtain natural language explanations for black box text modules.
1 code implementation • 4 May 2023 • Kaixin Ma, Hao Cheng, Yu Zhang, Xiaodong Liu, Eric Nyberg, Jianfeng Gao
Our approach outperforms recent self-supervised retrievers in zero-shot evaluations and achieves state-of-the-art fine-tuned retrieval performance on NQ, HotpotQA and OTT-QA.
Ranked #4 on Question Answering on HotpotQA
no code implementations • 1 May 2023 • Qiuyuan Huang, Jae Sung Park, Abhinav Gupta, Paul Bennett, Ran Gong, Subhojit Som, Baolin Peng, Owais Khan Mohammed, Chris Pal, Yejin Choi, Jianfeng Gao
In this study, we develop an infinite agent that learns to transfer knowledge memory from general foundation models (e. g. GPT4, DALLE) to novel domains or scenarios for scene understanding and generation in the physical or virtual world.
1 code implementation • NeurIPS 2023 • Pan Lu, Baolin Peng, Hao Cheng, Michel Galley, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Jianfeng Gao
At the heart of Chameleon is an LLM-based planner that assembles a sequence of tools to execute to generate the final response.
2 code implementations • NeurIPS 2023 • Xueyan Zou, Jianwei Yang, Hao Zhang, Feng Li, Linjie Li, JianFeng Wang, Lijuan Wang, Jianfeng Gao, Yong Jae Lee
In SEEM, we propose a novel decoding mechanism that enables diverse prompting for all types of segmentation tasks, aiming at a universal segmentation interface that behaves like large language models (LLMs).
2 code implementations • 6 Apr 2023 • Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, Jianfeng Gao
Prior work has shown that finetuning large language models (LLMs) using machine-generated instruction-following data enables such models to achieve remarkable zero-shot capabilities on new tasks, and no human-written instructions are needed.
no code implementations • 28 Mar 2023 • Sanxing Chen, Hao Cheng, Xiaodong Liu, Jian Jiao, Yangfeng Ji, Jianfeng Gao
Learning transferable representation of knowledge graphs (KGs) is challenging due to the heterogeneous, multi-relational nature of graph structures.
2 code implementations • ICCV 2023 • Hao Zhang, Feng Li, Xueyan Zou, Shilong Liu, Chunyuan Li, Jianfeng Gao, Jianwei Yang, Lei Zhang
We present OpenSeeD, a simple Open-vocabulary Segmentation and Detection framework that jointly learns from different segmentation and detection datasets.
Ranked #2 on Instance Segmentation on ADE20K val (using extra training data)
3 code implementations • 2 Mar 2023 • Sheng Zhang, Yanbo Xu, Naoto Usuyama, Hanwen Xu, Jaspreet Bagga, Robert Tinn, Sam Preston, Rajesh Rao, Mu Wei, Naveen Valluri, Cliff Wong, Andrea Tupini, Yu Wang, Matt Mazzola, Swadheen Shukla, Lars Liden, Jianfeng Gao, Matthew P. Lungren, Tristan Naumann, Sheng Wang, Hoifung Poon
Therefore, training an effective generalist biomedical model requires high-quality multimodal data, such as parallel image-text pairs.
Ranked #3 on Medical Visual Question Answering on SLAKE-English
no code implementations • 2 Mar 2023 • Felix Faltings, Michel Galley, Baolin Peng, Kianté Brantley, Weixin Cai, Yizhe Zhang, Jianfeng Gao, Bill Dolan
Unfortunately, this means most of the research on text, code, and image generation has focused on non-interactive settings, whereby the model is expected to get everything right without accounting for any input from a user who may be willing to help.
no code implementations • 24 Feb 2023 • Baolin Peng, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, Weizhu Chen, Jianfeng Gao
Large language models (LLMs), such as ChatGPT, are able to generate human-like, fluent responses for many downstream tasks, e. g., task-oriented dialog and question answering.
1 code implementation • CVPR 2023 • Haotian Liu, Kilho Son, Jianwei Yang, Ce Liu, Jianfeng Gao, Yong Jae Lee, Chunyuan Li
Image-text contrastive learning models such as CLIP have demonstrated strong task transfer ability.
Ranked #1 on Semi-Supervised Image Classification on ImageNet - 1% labeled data (using extra training data)
1 code implementation • CVPR 2023 • Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jianwei Yang, Jianfeng Gao, Chunyuan Li, Yong Jae Lee
Large-scale text-to-image diffusion models have made amazing advances.
Ranked #4 on Conditional Text-to-Image Synthesis on COCO-MIG
1 code implementation • CVPR 2023 • Xueyan Zou, Zi-Yi Dou, Jianwei Yang, Zhe Gan, Linjie Li, Chunyuan Li, Xiyang Dai, Harkirat Behl, JianFeng Wang, Lu Yuan, Nanyun Peng, Lijuan Wang, Yong Jae Lee, Jianfeng Gao
We present X-Decoder, a generalized decoding model that can predict pixel-level segmentation and language tokens seamlessly.
Ranked #4 on Instance Segmentation on ADE20K val (using extra training data)
1 code implementation • 21 Dec 2022 • Zonglin Yang, Li Dong, Xinya Du, Hao Cheng, Erik Cambria, Xiaodong Liu, Jianfeng Gao, Furu Wei
To this end, we propose a new paradigm (task) for inductive reasoning, which is to induce natural language rules from natural language facts, and create a dataset termed DEER containing 1. 2k rule-fact pairs for the task, where rules and facts are written in natural language.
no code implementations • 20 Dec 2022 • Yu Li, Baolin Peng, Pengcheng He, Michel Galley, Zhou Yu, Jianfeng Gao
In this work, we propose DIONYSUS (dynamic input optimization in pre-training for dialogue summarization), a pre-trained encoder-decoder model for summarizing dialogues in any new domain.
no code implementations • 20 Dec 2022 • Miaoran Li, Baolin Peng, Michel Galley, Jianfeng Gao, Zhu Zhang
To better mimic human-level conversations that usually fuse various dialog modes, it is essential to build a system that can effectively handle both TOD and ODD and access different knowledge sources.
1 code implementation • 15 Dec 2022 • Simiao Zuo, Xiaodong Liu, Jian Jiao, Denis Charles, Eren Manavoglu, Tuo Zhao, Jianfeng Gao
Specifically, we augment a SSM into the bottom layer of SPADE, and we employ efficient local attention methods for the other layers.
no code implementations • 4 Dec 2022 • Faeze Brahman, Baolin Peng, Michel Galley, Sudha Rao, Bill Dolan, Snigdha Chaturvedi, Jianfeng Gao
We propose a new grounded keys-to-text generation task: the task is to generate a factual description about an entity given a set of guiding keys, and grounding passages.
1 code implementation • 30 Nov 2022 • Qi Zhu, Christian Geishauser, Hsien-Chin Lin, Carel van Niekerk, Baolin Peng, Zheng Zhang, Michael Heck, Nurul Lubis, Dazhen Wan, Xiaochen Zhu, Jianfeng Gao, Milica Gašić, Minlie Huang
Task-oriented dialogue (TOD) systems function as digital assistants, guiding users through various tasks such as booking flights or finding restaurants.
1 code implementation • 25 Nov 2022 • Haotian Cui, Chenglong Wang, JunJie Huang, Jeevana Priya Inala, Todd Mytkowicz, Bo wang, Jianfeng Gao, Nan Duan
Our experiments show that (1) our refined training dataset lets models achieve better performance in the explanation generation tasks compared to larger unrefined data (15x larger), and (2) fine-tuned models can generate well-structured long docstrings comparable to human-written ones.
1 code implementation • 17 Nov 2022 • JunJie Huang, Chenglong Wang, Jipeng Zhang, Cong Yan, Haotian Cui, Jeevana Priya Inala, Colin Clement, Nan Duan, Jianfeng Gao
Code generation models can benefit data scientists' productivity by automatically generating code from context and text descriptions.
1 code implementation • 31 Oct 2022 • Yaqing Wang, Sahaj Agarwal, Subhabrata Mukherjee, Xiaodong Liu, Jing Gao, Ahmed Hassan Awadallah, Jianfeng Gao
Standard fine-tuning of large pre-trained language models (PLMs) for downstream tasks requires updating hundreds of millions to billions of parameters, and storing a large copy of the PLM weights for every task resulting in increased cost for storing, sharing and serving the models.
no code implementations • 25 Oct 2022 • Yufan Zhou, Chunyuan Li, Changyou Chen, Jianfeng Gao, Jinhui Xu
The low requirement of the proposed method yields high flexibility and usability: it can be beneficial to a wide range of settings, including the few-shot, semi-supervised and fully-supervised learning; it can be applied on different models including generative adversarial networks (GANs) and diffusion models.
2 code implementations • 22 Oct 2022 • Kaixin Ma, Hao Cheng, Xiaodong Liu, Eric Nyberg, Jianfeng Gao
We propose a novel open-domain question answering (ODQA) framework for answering single/multi-hop questions across heterogeneous knowledge sources.
1 code implementation • 17 Oct 2022 • Zhe Gan, Linjie Li, Chunyuan Li, Lijuan Wang, Zicheng Liu, Jianfeng Gao
This paper surveys vision-language pre-training (VLP) methods for multimodal intelligence that have been developed in the last few years.
1 code implementation • 14 Oct 2022 • Ganesh Jawahar, Subhabrata Mukherjee, Xiaodong Liu, Young Jin Kim, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan, Ahmed Hassan Awadallah, Sebastien Bubeck, Jianfeng Gao
Furthermore, existing MoE works do not consider computational constraints (e. g., FLOPs, latency) to guide their design.
1 code implementation • 11 Oct 2022 • Hao Cheng, Hao Fang, Xiaodong Liu, Jianfeng Gao
Given its effectiveness on knowledge-intensive natural language processing tasks, dense retrieval models have become increasingly popular.
2 code implementations • 4 Oct 2022 • Chandan Singh, John X. Morris, Jyoti Aneja, Alexander M. Rush, Jianfeng Gao
Large language models (LLMs) have displayed an impressive ability to harness natural language to perform complex tasks.
4 code implementations • 23 Sep 2022 • Chandan Singh, Armin Askari, Rich Caruana, Jianfeng Gao
Recent large language models (LLMs) have demonstrated remarkable prediction performance for a growing array of tasks.
1 code implementation • 30 Aug 2022 • Sheng Zhang, Hao Cheng, Jianfeng Gao, Hoifung Poon
We present a bi-encoder framework for named entity recognition (NER), which applies contrastive learning to map candidate text spans and entity types into the same vector representation space.
Ranked #1 on Named Entity Recognition (NER) on BC5CDR
1 code implementation • 21 Aug 2022 • Pengcheng He, Baolin Peng, Liyang Lu, Song Wang, Jie Mei, Yang Liu, Ruochen Xu, Hany Hassan Awadalla, Yu Shi, Chenguang Zhu, Wayne Xiong, Michael Zeng, Jianfeng Gao, Xuedong Huang
Z-Code++ creates new state of the art on 9 out of 13 text summarization tasks across 5 languages.
no code implementations • 11 Aug 2022 • Shuvendu K. Lahiri, Sarah Fakhoury, Aaditya Naik, Georgios Sakkas, Saikat Chakraborty, Madanlal Musuvathi, Piali Choudhury, Curtis von Veh, Jeevana Priya Inala, Chenglong Wang, Jianfeng Gao
Large language models (LLMs) have shown great potential in automating significant aspects of coding by producing natural code from informal natural language (NL) intent.
no code implementations • MTSummit 2021 • Paul Soulos, Sudha Rao, Caitlin Smith, Eric Rosen, Asli Celikyilmaz, R. Thomas McCoy, Yichen Jiang, Coleman Haley, Roland Fernandez, Hamid Palangi, Jianfeng Gao, Paul Smolensky
Machine translation has seen rapid progress with the advent of Transformer-based models.
1 code implementation • 24 Jun 2022 • Miaoran Li, Baolin Peng, Jianfeng Gao, Zhu Zhang
Existing studies in conversational AI mostly treat task-oriented dialog (TOD) and question answering (QA) as separate tasks.
1 code implementation • 22 Jun 2022 • Baolin Peng, Michel Galley, Pengcheng He, Chris Brockett, Lars Liden, Elnaz Nouri, Zhou Yu, Bill Dolan, Jianfeng Gao
We introduce GODEL (Grounded Open Dialogue Language Model), a large pre-trained language model for dialog.
1 code implementation • NeurIPS 2022 • Zi-Yi Dou, Aishwarya Kamath, Zhe Gan, Pengchuan Zhang, JianFeng Wang, Linjie Li, Zicheng Liu, Ce Liu, Yann Lecun, Nanyun Peng, Jianfeng Gao, Lijuan Wang
Vision-language (VL) pre-training has recently received considerable attention.
Ranked #1 on Phrase Grounding on Flickr30k Entities Dev
1 code implementation • 12 Jun 2022 • Haotian Zhang, Pengchuan Zhang, Xiaowei Hu, Yen-Chun Chen, Liunian Harold Li, Xiyang Dai, Lijuan Wang, Lu Yuan, Jenq-Neng Hwang, Jianfeng Gao
We present GLIPv2, a grounded VL understanding model, that serves both localization tasks (e. g., object detection, instance segmentation) and Vision-Language (VL) understanding tasks (e. g., VQA, image captioning).
Ranked #1 on Phrase Grounding on Flickr30k Entities Test (using extra training data)
1 code implementation • 4 Jun 2022 • Jeevana Priya Inala, Chenglong Wang, Mei Yang, Andres Codas, Mark Encarnación, Shuvendu K Lahiri, Madanlal Musuvathi, Jianfeng Gao
Large language models (LLMs) have demonstrated an impressive ability to generate code for various programming tasks.
1 code implementation • 28 May 2022 • Ansong Ni, Jeevana Priya Inala, Chenglong Wang, Oleksandr Polozov, Christopher Meek, Dragomir Radev, Jianfeng Gao
We show that our use of self-sampled correct and partially-correct solutions can benefit learning and help guide the sampling process, leading to more efficient exploration of the solution space.
Ranked #139 on Arithmetic Reasoning on GSM8K
1 code implementation • 24 May 2022 • Yaqing Wang, Sahaj Agarwal, Subhabrata Mukherjee, Xiaodong Liu, Jing Gao, Ahmed Hassan Awadallah, Jianfeng Gao
Standard fine-tuning of large pre-trained language models (PLMs) for downstream tasks requires updating hundreds of millions to billions of parameters, and storing a large copy of the PLM weights for every task resulting in increased cost for storing, sharing and serving the models.
1 code implementation • 20 May 2022 • Weizhi Wang, Li Dong, Hao Cheng, Haoyu Song, Xiaodong Liu, Xifeng Yan, Jianfeng Gao, Furu Wei
With the visually-augmented context, VaLM uses a visual knowledge fusion layer to enable multimodal grounded language modeling by attending to both text context and visual knowledge in images.
1 code implementation • 19 May 2022 • Liangke Gui, Yingshan Chang, Qiuyuan Huang, Subhojit Som, Alex Hauptmann, Jianfeng Gao, Yonatan Bisk
Vision-Language Transformers can be learned without low-level human labels (e. g. class labels, bounding boxes, etc).
no code implementations • 2 May 2022 • Paul Smolensky, R. Thomas McCoy, Roland Fernandez, Matthew Goldrick, Jianfeng Gao
What explains the dramatic progress from 20th-century to 21st-century AI, and how can the remaining limitations of current AI be overcome?
2 code implementations • 20 Apr 2022 • Sheng Shen, Chunyuan Li, Xiaowei Hu, Jianwei Yang, Yujia Xie, Pengchuan Zhang, Zhe Gan, Lijuan Wang, Lu Yuan, Ce Liu, Kurt Keutzer, Trevor Darrell, Anna Rohrbach, Jianfeng Gao
We propose K-LITE, a simple strategy to leverage external knowledge for building transferable visual systems: In training, it enriches entities in text with WordNet and Wiktionary knowledge, leading to an efficient and scalable approach to learning image representations that uses knowledge about the visual concepts.
8 code implementations • 19 Apr 2022 • Chunyuan Li, Haotian Liu, Liunian Harold Li, Pengchuan Zhang, Jyoti Aneja, Jianwei Yang, Ping Jin, Houdong Hu, Zicheng Liu, Yong Jae Lee, Jianfeng Gao
In general, these language-augmented visual models demonstrate strong transferability to a variety of datasets and tasks.
Ranked #1 on Object Detection on ELEVATER
no code implementations • 16 Apr 2022 • Shashank Gupta, Subhabrata Mukherjee, Krishan Subudhi, Eduardo Gonzalez, Damien Jose, Ahmed H. Awadallah, Jianfeng Gao
Traditional multi-task learning (MTL) methods use dense networks that use the same set of shared weights across several different tasks.
no code implementations • 13 Apr 2022 • Payal Bajaj, Chenyan Xiong, Guolin Ke, Xiaodong Liu, Di He, Saurabh Tiwary, Tie-Yan Liu, Paul Bennett, Xia Song, Jianfeng Gao
We present an efficient method of pretraining large-scale autoencoding language models using training signals generated by an auxiliary model.
1 code implementation • CVPR 2022 • Jianwei Yang, Chunyuan Li, Pengchuan Zhang, Bin Xiao, Ce Liu, Lu Yuan, Jianfeng Gao
Particularly, it attains gains up to 9. 2% and 14. 5% in average on zero-shot recognition benchmarks over the language-image contrastive learning and supervised learning methods, respectively.
6 code implementations • 22 Mar 2022 • Jianwei Yang, Chunyuan Li, Xiyang Dai, Lu Yuan, Jianfeng Gao
For semantic segmentation with UPerNet, FocalNet base at single-scale outperforms Swin by 2. 4, and beats Swin at multi-scale (50. 5 v. s.
Ranked #8 on Object Detection on COCO minival (using extra training data)
3 code implementations • 7 Mar 2022 • Greg Yang, Edward J. Hu, Igor Babuschkin, Szymon Sidor, Xiaodong Liu, David Farhi, Nick Ryder, Jakub Pachocki, Weizhu Chen, Jianfeng Gao
Hyperparameter (HP) tuning in deep learning is an expensive process, prohibitively so for neural networks (NNs) with billions of parameters.
no code implementations • 17 Feb 2022 • Da Yin, Li Dong, Hao Cheng, Xiaodong Liu, Kai-Wei Chang, Furu Wei, Jianfeng Gao
With the increasing of model capacity brought by pre-trained language models, there emerges boosting needs for more knowledgeable natural language processing (NLP) models with advanced functionalities including providing and making flexible use of encyclopedic and commonsense knowledge.
1 code implementation • ICLR 2022 • Chen Liang, Haoming Jiang, Simiao Zuo, Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen, Tuo Zhao
Analysis shows that the proposed schedule indeed reduces the redundancy and improves generalization performance.
no code implementations • 29 Jan 2022 • Dongkuan Xu, Subhabrata Mukherjee, Xiaodong Liu, Debadeepta Dey, Wenhui Wang, Xiang Zhang, Ahmed Hassan Awadallah, Jianfeng Gao
Our framework AutoDistil addresses above challenges with the following steps: (a) Incorporates inductive bias and heuristics to partition Transformer search space into K compact sub-spaces (K=3 for typical student sizes of base, small and tiny); (b) Trains one SuperLM for each sub-space using task-agnostic objective (e. g., self-attention distillation) with weight-sharing of students; (c) Lightweight search for the optimal student without re-training.
no code implementations • SIGDIAL (ACL) 2022 • Xiaoying Zhang, Baolin Peng, Jianfeng Gao, Helen Meng
In this paper, we study the problem of automatically adapting task bots to changing environments by learning from human-bot interactions with minimum or zero human annotations.
no code implementations • 13 Jan 2022 • Jianfeng Gao, Chenyan Xiong, Paul Bennett, Nick Craswell
A conversational information retrieval (CIR) system is an information retrieval (IR) system with a conversational interface which allows users to interact with the system to seek information via multi-turn conversations of natural language, in spoken or written form.
1 code implementation • NAACL 2022 • Liangke Gui, Borui Wang, Qiuyuan Huang, Alex Hauptmann, Yonatan Bisk, Jianfeng Gao
The primary focus of recent work with largescale transformers has been on optimizing the amount of information packed into the model's parameters.
1 code implementation • CVPR 2022 • Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, Jianfeng Gao
However, we show that directly applying such models to recognize image regions for object detection leads to poor performance due to a domain shift: CLIP was trained to match an image as a whole to a text description, without capturing the fine-grained alignment between image regions and text spans.
Ranked #11 on Open Vocabulary Object Detection on MSCOCO (using extra training data)
no code implementations • NAACL 2022 • Yu Li, Baolin Peng, Yelong Shen, Yi Mao, Lars Liden, Zhou Yu, Jianfeng Gao
To address these challenges, we present PLUG, a language model that homogenizes different knowledge sources to a unified knowledge representation for knowledge-grounded dialogue generation tasks.
no code implementations • 15 Dec 2021 • Robert Tinn, Hao Cheng, Yu Gu, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon
Overall, domainspecific vocabulary and pretraining facilitate more robust models for fine-tuning.
no code implementations • 15 Dec 2021 • Sheng Zhang, Hao Cheng, Shikhar Vashishth, Cliff Wong, Jinfeng Xiao, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon
Zero-shot entity linking has emerged as a promising direction for generalizing to new entities, but it still requires example gold entity mentions during training and canonical descriptions for all entities, both of which are rarely available outside of Wikipedia.
no code implementations • 12 Dec 2021 • Liang Qiu, Yizhou Zhao, Jinchao Li, Pan Lu, Baolin Peng, Jianfeng Gao, Song-Chun Zhu
To the best of our knowledge, ValueNet is the first large-scale text dataset for human value modeling, and we are the first one trying to incorporate a value model into emotionally intelligent dialogue systems.
2 code implementations • CVPR 2022 • Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, Kai-Wei Chang, Jianfeng Gao
The unification brings two benefits: 1) it allows GLIP to learn from both detection and grounding data to improve both tasks and bootstrap a good grounding model; 2) GLIP can leverage massive image-text pairs by generating grounding boxes in a self-training fashion, making the learned representation semantic-rich.
Ranked #1 on 2D Object Detection on RF100
2 code implementations • 6 Dec 2021 • Yichong Xu, Chenguang Zhu, Shuohang Wang, Siqi Sun, Hao Cheng, Xiaodong Liu, Jianfeng Gao, Pengcheng He, Michael Zeng, Xuedong Huang
In particular, we focus on the task of Commonsense Reasoning, demonstrating that the proposed external attention mechanism can augment existing transformer models and significantly improve the model's reasoning capabilities.
Ranked #1 on Common Sense Reasoning on CommonsenseQA (using extra training data)
1 code implementation • NeurIPS 2021 • Ge Yang, Edward Hu, Igor Babuschkin, Szymon Sidor, Xiaodong Liu, David Farhi, Nick Ryder, Jakub Pachocki, Weizhu Chen, Jianfeng Gao
Hyperparameter (HP) tuning in deep learning is an expensive process, prohibitively so for neural networks (NNs) with billions of parameters. We show that, in the recently discovered Maximal Update Parametrization ($\mu$P), many optimal HPs remain stable even as model size changes.
1 code implementation • NeurIPS 2021 • Jianwei Yang, Chunyuan Li, Pengchuan Zhang, Xiyang Dai, Bin Xiao, Lu Yuan, Jianfeng Gao
With focal attention, we propose a new variant of Vision Transformer models, called Focal Transformers, which achieve superior performance over the state-of-the-art (SoTA) Vision Transformers on a range of public image classification and object detection benchmarks.
1 code implementation • 22 Nov 2021 • Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, Ce Liu, Mengchen Liu, Zicheng Liu, Yumao Lu, Yu Shi, Lijuan Wang, JianFeng Wang, Bin Xiao, Zhen Xiao, Jianwei Yang, Michael Zeng, Luowei Zhou, Pengchuan Zhang
Computer vision foundation models, which are trained on diverse, large-scale dataset and can be adapted to a wide range of downstream tasks, are critical for this mission to solve real-world computer vision applications.
Ranked #1 on Action Recognition In Videos on Kinetics-600
no code implementations • 18 Nov 2021 • R. Thomas McCoy, Paul Smolensky, Tal Linzen, Jianfeng Gao, Asli Celikyilmaz
We apply these analyses to four neural language models (an LSTM, a Transformer, Transformer-XL, and GPT-2).
2 code implementations • 18 Nov 2021 • Pengcheng He, Jianfeng Gao, Weizhu Chen
We thus propose a new gradient-disentangled embedding sharing method that avoids the tug-of-war dynamics, improving both training efficiency and the quality of the pre-trained model.
Ranked #1 on Natural Language Inference on MRPC
Natural Language Inference Natural Language Understanding +2
1 code implementation • 4 Nov 2021 • Subhabrata Mukherjee, Xiaodong Liu, Guoqing Zheng, Saghar Hosseini, Hao Cheng, Greg Yang, Christopher Meek, Ahmed Hassan Awadallah, Jianfeng Gao
We demonstrate that while recent models reach human performance when they have access to large amounts of labeled data, there is a huge gap in performance in the few-shot setting for most tasks.
1 code implementation • 4 Nov 2021 • Boxin Wang, Chejian Xu, Shuohang Wang, Zhe Gan, Yu Cheng, Jianfeng Gao, Ahmed Hassan Awadallah, Bo Li
In this paper, we present Adversarial GLUE (AdvGLUE), a new multi-task benchmark to quantitatively and thoroughly explore and evaluate the vulnerabilities of modern large-scale language models under various types of adversarial attacks.
Ranked #1 on Adversarial Robustness on AdvGLUE
no code implementations • 21 Oct 2021 • Baolin Peng, Chunyuan Li, Zhu Zhang, Jinchao Li, Chenguang Zhu, Jianfeng Gao
We propose SYNERGY, a hybrid learning framework where a task bot is developed in two steps: (i) Symbolic knowledge to neural networks: Large amounts of simulated dialog sessions are generated based on task-specific symbolic knowledge which is represented as a task schema consisting of dialog flows and task-oriented databases.
1 code implementation • ACL 2022 • Kaixin Ma, Hao Cheng, Xiaodong Liu, Eric Nyberg, Jianfeng Gao
The retriever-reader framework is popular for open-domain question answering (ODQA) due to its ability to use explicit knowledge.
1 code implementation • Findings (NAACL) 2022 • Yaqing Wang, Subhabrata Mukherjee, Xiaodong Liu, Jing Gao, Ahmed Hassan Awadallah, Jianfeng Gao
The first is the use of self-training to leverage large amounts of unlabeled data for prompt-based FN in few-shot settings.
1 code implementation • ICLR 2022 • Simiao Zuo, Xiaodong Liu, Jian Jiao, Young Jin Kim, Hany Hassan, Ruofei Zhang, Tuo Zhao, Jianfeng Gao
While most on-going research focuses on improving SAMs models by exploring methods of routing inputs to experts, our analysis reveals that such research might not lead to the solution we expect, i. e., the commonly-used routing methods based on gating mechanisms do not work better than randomly routing inputs to experts.
1 code implementation • Findings (EMNLP) 2021 • Simiao Zuo, Chen Liang, Haoming Jiang, Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen, Tuo Zhao
Adversarial regularization can improve model generalization in many natural language processing tasks.
2 code implementations • CVPR 2022 • Yingshan Chang, Mridu Narang, Hisami Suzuki, Guihong Cao, Jianfeng Gao, Yonatan Bisk
Scaling Visual Question Answering (VQA) to the open-domain and multi-hop nature of web searches, requires fundamental advances in visual representation learning, knowledge aggregation, and language generation.
no code implementations • ICCV 2021 • Jianwei Yang, Yonatan Bisk, Jianfeng Gao
This is motivated by the observation that for a video-text pair, the content words in the text, such as nouns and verbs, are more likely to be aligned with the visual contents in the video than the function words.
Ranked #3 on Temporal Action Localization on CrossTask (using extra training data)
1 code implementation • ACL 2021 • Shiyue Zhang, Asli Celikyilmaz, Jianfeng Gao, Mohit Bansal
Furthermore, we find that widely used automatic evaluation metrics (ROUGE, BERTScore) are weakly correlated with human judgments on this email thread summarization task.
Ranked #1 on Email Thread Summarization on EmailSum (short)
1 code implementation • 27 Jul 2021 • Xiaotian Han, Jianwei Yang, Houdong Hu, Lei Zhang, Jianfeng Gao, Pengchuan Zhang
There is a surge of interest in image scene graph generation (object, attribute and relationship detection) due to the need of building fine-grained image understanding models that go beyond object detection.
3 code implementations • 1 Jul 2021 • Jianwei Yang, Chunyuan Li, Pengchuan Zhang, Xiyang Dai, Bin Xiao, Lu Yuan, Jianfeng Gao
With focal self-attention, we propose a new variant of Vision Transformer models, called Focal Transformer, which achieves superior performance over the state-of-the-art vision Transformers on a range of public image classification and object detection benchmarks.
Ranked #17 on Instance Segmentation on COCO test-dev
no code implementations • 25 Jun 2021 • Yu Wang, Jinchao Li, Tristan Naumann, Chenyan Xiong, Hao Cheng, Robert Tinn, Cliff Wong, Naoto Usuyama, Richard Rogahn, Zhihong Shen, Yang Qin, Eric Horvitz, Paul N. Bennett, Jianfeng Gao, Hoifung Poon
A prominent case in point is the explosion of the biomedical literature on COVID-19, which swelled to hundreds of thousands of papers in a matter of months.
1 code implementation • ICLR 2022 • Chunyuan Li, Jianwei Yang, Pengchuan Zhang, Mei Gao, Bin Xiao, Xiyang Dai, Lu Yuan, Jianfeng Gao
This paper investigates two techniques for developing efficient self-supervised vision transformers (EsViT) for visual representation learning.
Ranked #16 on Self-Supervised Image Classification on ImageNet
Representation Learning Self-Supervised Image Classification
1 code implementation • 8 Jun 2021 • Subhabrata Mukherjee, Ahmed Hassan Awadallah, Jianfeng Gao
While deep and large pre-trained models are the state-of-the-art for various natural language processing tasks, their huge size poses significant challenges for practical uses in resource constrained settings.
1 code implementation • NAACL 2021 • Yichen Jiang, Asli Celikyilmaz, Paul Smolensky, Paul Soulos, Sudha Rao, Hamid Palangi, Roland Fernandez, Caitlin Smith, Mohit Bansal, Jianfeng Gao
On several syntactic and semantic probing tasks, we demonstrate the emergent structural information in the role vectors and improved syntactic interpretability in the TPR layer outputs.
1 code implementation • 19 May 2021 • Jacob Russin, Roland Fernandez, Hamid Palangi, Eric Rosen, Nebojsa Jojic, Paul Smolensky, Jianfeng Gao
A longstanding question in cognitive science concerns the learning mechanisms underlying compositionality in human cognition.
1 code implementation • 14 May 2021 • Yizhe Zhang, Siqi Sun, Xiang Gao, Yuwei Fang, Chris Brockett, Michel Galley, Jianfeng Gao, Bill Dolan
We propose a framework that alleviates this data constraint by jointly training a grounded generator and document retriever on the language model signal.
1 code implementation • NAACL 2021 • Lis Pereira, Xiaodong Liu, Hao Cheng, Hoifung Poon, Jianfeng Gao, Ichiro Kobayashi
We present a simple yet effective Targeted Adversarial Training (TAT) algorithm to improve adversarial training for natural language understanding.
1 code implementation • EMNLP 2021 • Simiao Zuo, Chen Liang, Haoming Jiang, Xiaodong Liu, Pengcheng He, Jianfeng Gao, Weizhu Chen, Tuo Zhao
Adversarial regularization has been shown to improve the generalization performance of deep learning models in various natural language processing tasks.
3 code implementations • ICCV 2021 • Pengchuan Zhang, Xiyang Dai, Jianwei Yang, Bin Xiao, Lu Yuan, Lei Zhang, Jianfeng Gao
This paper presents a new Vision Transformer (ViT) architecture Multi-Scale Vision Longformer, which significantly enhances the ViT of \cite{dosovitskiy2020image} for encoding high-resolution images using two techniques.
Ranked #45 on Instance Segmentation on COCO minival
no code implementations • Findings (EMNLP) 2021 • Chen Liang, Haoming Jiang, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao, Tuo Zhao
Existing curriculum learning approaches to Neural Machine Translation (NMT) require sampling sufficient amounts of "easy" samples from training data at the early training stage.
1 code implementation • 2 Mar 2021 • Ramakanth Pasunuru, Asli Celikyilmaz, Michel Galley, Chenyan Xiong, Yizhe Zhang, Mohit Bansal, Jianfeng Gao
The progress in Query-focused Multi-Document Summarization (QMDS) has been limited by the lack of sufficient largescale high-quality training datasets.
1 code implementation • 24 Feb 2021 • You Zhou, Jianfeng Gao, Tamim Asfour
For multiple modes, we suggest to learn local latent representations of motion trajectories with a density estimation method based on real-valued non-volume preserving (RealNVP) transformations that provides a set of powerful, stably invertible, and learnable transformations.
7 code implementations • CVPR 2021 • Pengchuan Zhang, Xiujun Li, Xiaowei Hu, Jianwei Yang, Lei Zhang, Lijuan Wang, Yejin Choi, Jianfeng Gao
In our experiments we feed the visual features generated by the new object detection model into a Transformer-based VL fusion model \oscar \cite{li2020oscar}, and utilize an improved approach \short\ to pre-train the VL model and fine-tune it on a wide range of downstream VL tasks.
Ranked #2 on Image-text matching on CommercialAdsDataset
no code implementations • ACL 2021 • Hao Cheng, Yelong Shen, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao
To date, most of recent work under the retrieval-reader framework for open-domain QA focuses on either extractive or generative reader exclusively.
Ranked #1 on Open-Domain Question Answering on TriviaQA
no code implementations • 1 Jan 2021 • Sewon Min, Jordan Boyd-Graber, Chris Alberti, Danqi Chen, Eunsol Choi, Michael Collins, Kelvin Guu, Hannaneh Hajishirzi, Kenton Lee, Jennimaria Palomaki, Colin Raffel, Adam Roberts, Tom Kwiatkowski, Patrick Lewis, Yuxiang Wu, Heinrich Küttler, Linqing Liu, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel, Sohee Yang, Minjoon Seo, Gautier Izacard, Fabio Petroni, Lucas Hosseini, Nicola De Cao, Edouard Grave, Ikuya Yamada, Sonse Shimaoka, Masatoshi Suzuki, Shumpei Miyawaki, Shun Sato, Ryo Takahashi, Jun Suzuki, Martin Fajcik, Martin Docekal, Karel Ondrej, Pavel Smrz, Hao Cheng, Yelong Shen, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao, Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, Wen-tau Yih
We review the EfficientQA competition from NeurIPS 2020.
1 code implementation • 1 Jan 2021 • Yuning Mao, Pengcheng He, Xiaodong Liu, Yelong Shen, Jianfeng Gao, Jiawei Han, Weizhu Chen
Current open-domain question answering systems often follow a Retriever-Reader architecture, where the retriever first retrieves relevant passages and the reader then reads the retrieved passages to form an answer.
no code implementations • 1 Jan 2021 • Jianwei Yang, Yonatan Bisk, Jianfeng Gao
Building video and language understanding models requires grounding linguistic concepts and video contents into a shared space.
2 code implementations • 29 Dec 2020 • Jiaxin Huang, Chunyuan Li, Krishan Subudhi, Damien Jose, Shobana Balakrishnan, Weizhu Chen, Baolin Peng, Jianfeng Gao, Jiawei Han
This paper presents a comprehensive study to efficiently build named entity recognition (NER) systems when a small number of in-domain labeled data is available.
no code implementations • ACL 2021 • Baolin Peng, Chunyuan Li, Zhu Zhang, Chenguang Zhu, Jinchao Li, Jianfeng Gao
For task-oriented dialog systems to be maximally useful, it must be able to process conversations in a way that is (1) generalizable with a small number of training examples for new task domains, and (2) robust to user input in various styles, modalities or domains.
no code implementations • 25 Dec 2020 • Chunyuan Li, Xiujun Li, Lei Zhang, Baolin Peng, Mingyuan Zhou, Jianfeng Gao
Self-supervised pre-training (SSP) employs random image transformations to generate training data for visual representation learning.
Ranked #69 on Self-Supervised Image Classification on ImageNet
no code implementations • 13 Dec 2020 • JianFeng Wang, Xiaowei Hu, Pengchuan Zhang, Xiujun Li, Lijuan Wang, Lei Zhang, Jianfeng Gao, Zicheng Liu
We design a Two-stage Efficient feature Extractor (TEE), inspired by the one-stage EfficientDet network, to significantly reduce the time cost of visual feature extraction by $95\%$, compared to a baseline model.
1 code implementation • 18 Nov 2020 • Hassan Akbari, Hamid Palangi, Jianwei Yang, Sudha Rao, Asli Celikyilmaz, Roland Fernandez, Paul Smolensky, Jianfeng Gao, Shih-Fu Chang
In this paper, we propose a new model architecture for learning multi-modal neuro-symbolic representations for video captioning.
no code implementations • 12 Nov 2020 • Chulaka Gunasekara, Seokhwan Kim, Luis Fernando D'Haro, Abhinav Rastogi, Yun-Nung Chen, Mihail Eric, Behnam Hedayatnia, Karthik Gopalakrishnan, Yang Liu, Chao-Wei Huang, Dilek Hakkani-Tür, Jinchao Li, Qi Zhu, Lingxiao Luo, Lars Liden, Kaili Huang, Shahin Shayandeh, Runze Liang, Baolin Peng, Zheng Zhang, Swadheen Shukla, Minlie Huang, Jianfeng Gao, Shikib Mehri, Yulan Feng, Carla Gordon, Seyed Hossein Alavi, David Traum, Maxine Eskenazi, Ahmad Beirami, Eunjoon, Cho, Paul A. Crook, Ankita De, Alborz Geramifard, Satwik Kottur, Seungwhan Moon, Shivani Poddar, Rajen Subba
Interactive evaluation of dialog, and 4.
3 code implementations • 3 Nov 2020 • Chenyan Xiong, Zhenghao Liu, Si Sun, Zhuyun Dai, Kaitao Zhang, Shi Yu, Zhiyuan Liu, Hoifung Poon, Jianfeng Gao, Paul Bennett
Neural rankers based on deep pretrained language models (LMs) have been shown to improve many information retrieval benchmarks.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Ziming Li, Sungjin Lee, Baolin Peng, Jinchao Li, Julia Kiseleva, Maarten de Rijke, Shahin Shayandeh, Jianfeng Gao
Reinforcement learning methods have emerged as a popular choice for training an efficient and effective dialogue policy.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Homero Roman Roman, Yonatan Bisk, Jesse Thomason, Asli Celikyilmaz, Jianfeng Gao
In this paper, we go beyond instruction following and introduce a two-agent task where one agent navigates and asks questions that a second, guiding agent answers.
no code implementations • NAACL 2021 • Felix Faltings, Michel Galley, Gerold Hintz, Chris Brockett, Chris Quirk, Jianfeng Gao, Bill Dolan
A prevailing paradigm in neural text generation is one-shot generation, where text is produced in a single step.
no code implementations • Findings (ACL) 2021 • Saadia Gabriel, Asli Celikyilmaz, Rahul Jha, Yejin Choi, Jianfeng Gao
While neural language models can generate text with remarkable fluency and coherence, controlling for factual correctness in generation remains an open research question.
2 code implementations • NAACL 2021 • Hao Cheng, Xiaodong Liu, Lis Pereira, YaoLiang Yu, Jianfeng Gao
Theoretically, we provide a connection of two recent methods, Jacobian Regularization and Virtual Adversarial Training, under this framework.
no code implementations • 3 Oct 2020 • Yi Wei, Zhe Gan, Wenbo Li, Siwei Lyu, Ming-Ching Chang, Lei Zhang, Jianfeng Gao, Pengchuan Zhang
We present Mask-guided Generative Adversarial Network (MagGAN) for high-resolution face attribute editing, in which semantic facial masks from a pre-trained face parser are used to guide the fine-grained image editing process.
no code implementations • 28 Sep 2020 • Xiaowei Hu, Xi Yin, Kevin Lin, Lijuan Wang, Lei Zhang, Jianfeng Gao, Zicheng Liu
It is highly desirable yet challenging to generate image captions that can describe novel objects which are unseen in caption-labeled training data, a capability that is evaluated in the novel object captioning challenge (nocaps).
Ranked #3 on Image Captioning on nocaps-XD out-of-domain
1 code implementation • ACL 2021 • Yuning Mao, Pengcheng He, Xiaodong Liu, Yelong Shen, Jianfeng Gao, Jiawei Han, Weizhu Chen
We demonstrate that the generated contexts substantially enrich the semantics of the queries and GAR with sparse representations (BM25) achieves comparable or better performance than state-of-the-art dense retrieval methods such as DPR.
Ranked #9 on Passage Retrieval on Natural Questions
no code implementations • 7 Sep 2020 • Jianfeng Gao, Baolin Peng, Chunyuan Li, Jinchao Li, Shahin Shayandeh, Lars Liden, Heung-Yeung Shum
This article provides an overview of this progress and discusses related methods and technologies that can be incorporated for building robust conversational AI systems.
3 code implementations • EMNLP 2021 • Sanxing Chen, Xiaodong Liu, Jianfeng Gao, Jian Jiao, Ruofei Zhang, Yangfeng Ji
Our proposed model consists of two different Transformer blocks: the bottom block extracts features of each entity-relation pair in the local neighborhood of the source entity and the top block aggregates the relational information from outputs of the bottom block.
Ranked #1 on Link Prediction on FB15k-237 (Hit@10 metric)
4 code implementations • 18 Aug 2020 • Xiaodong Liu, Kevin Duh, Liyuan Liu, Jianfeng Gao
We explore the application of very deep Transformer models for Neural Machine Translation (NMT).
Ranked #1 on Machine Translation on WMT2014 English-French (using extra training data)
1 code implementation • 31 Jul 2020 • Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon
In this paper, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models.
Ranked #2 on Participant Intervention Comparison Outcome Extraction on EBM-NLP (using extra training data)
2 code implementations • ACL 2020 • Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, Ming Zhou
News recommendation is an important technique for personalized news service.
1 code implementation • ACL 2020 • Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan
We present a large, tunable neural conversational response generation model, DIALOGPT (dialogue generative pre-trained transformer).
no code implementations • ACL 2020 • Swadheen Shukla, Lars Liden, Shay, Shahin eh, Eslam Kamal, Jinchao Li, Matt Mazzola, Thomas Park, Baolin Peng, Jianfeng Gao
Traditionally, industry solutions for building a task-oriented dialog system have relied on helping dialog authors define rule-based dialog managers, represented as dialog flows.
no code implementations • 26 Jun 2020 • Asli Celikyilmaz, Elizabeth Clark, Jianfeng Gao
The paper surveys evaluation methods of natural language generation (NLG) systems that have been developed in the last few years.
1 code implementation • 9 Jun 2020 • Shi Yu, Jiahua Liu, Jingqin Yang, Chenyan Xiong, Paul Bennett, Jianfeng Gao, Zhiyuan Liu
Conversational query rewriting aims to reformulate a concise conversational query to a fully specified, context-independent query that can be effectively handled by existing information retrieval systems.
9 code implementations • ICLR 2021 • Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen
Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks.
Ranked #1 on Common Sense Reasoning on SWAG
1 code implementation • CVPR 2021 • Minheng Ni, Haoyang Huang, Lin Su, Edward Cui, Taroon Bharti, Lijuan Wang, Jianfeng Gao, Dongdong Zhang, Nan Duan
We present M3P, a Multitask Multilingual Multimodal Pre-trained model that combines multilingual pre-training and multimodal pre-training into a unified framework via multitask pre-training.
no code implementations • 22 May 2020 • Yuhang Song, Wenbo Li, Lei Zhang, Jianwei Yang, Emre Kiciman, Hamid Palangi, Jianfeng Gao, C. -C. Jay Kuo, Pengchuan Zhang
We study in this paper the problem of novel human-object interaction (HOI) detection, aiming at improving the generalization ability of the model to unseen scenarios.
no code implementations • SIGDIAL (ACL) 2020 • Ryuichi Takanobu, Qi Zhu, Jinchao Li, Baolin Peng, Jianfeng Gao, Minlie Huang
There is a growing interest in developing goal-oriented dialog systems which serve users in accomplishing complex tasks through multi-turn conversations.
1 code implementation • 11 May 2020 • Baolin Peng, Chunyuan Li, Jinchao Li, Shahin Shayandeh, Lars Liden, Jianfeng Gao
We present a new method SOLOIST that uses transfer learning and machine teaching to build task bots at scale.
Ranked #4 on End-To-End Dialogue Modelling on MULTIWOZ 2.0
1 code implementation • 2 May 2020 • Homero Roman Roman, Yonatan Bisk, Jesse Thomason, Asli Celikyilmaz, Jianfeng Gao
In this paper, we go beyond instruction following and introduce a two-agent task where one agent navigates and asks questions that a second, guiding agent answers.
1 code implementation • 1 May 2020 • Zeqiu Wu, Michel Galley, Chris Brockett, Yizhe Zhang, Xiang Gao, Chris Quirk, Rik Koncel-Kedziorski, Jianfeng Gao, Hannaneh Hajishirzi, Mari Ostendorf, Bill Dolan
Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting responses.
no code implementations • 1 May 2020 • Jianfeng Gao, You Zhou, Tamim Asfour
Compliant robot behavior is crucial for the realization of contact-rich manipulation tasks.
1 code implementation • ICLR 2020 • Sam Lobel*, Chunyuan Li*, Jianfeng Gao, Lawrence Carin
We investigate new methods for training collaborative filtering models based on actor-critic reinforcement learning, to more directly maximize ranking-based objective functions.
2 code implementations • EMNLP 2020 • Hannah Rashkin, Asli Celikyilmaz, Yejin Choi, Jianfeng Gao
We propose the task of outline-conditioned story generation: given an outline as a set of phrases that describe key characters and events to appear in a story, the task is to generate a coherent narrative that is consistent with the provided outline.
1 code implementation • 29 Apr 2020 • Baolin Peng, Chenguang Zhu, Michael Zeng, Jianfeng Gao
The training of spoken language understanding (SLU) models often faces the problem of data scarcity.
3 code implementations • 20 Apr 2020 • Xiaodong Liu, Hao Cheng, Pengcheng He, Weizhu Chen, Yu Wang, Hoifung Poon, Jianfeng Gao
In natural language processing (NLP), pre-training large neural language models such as BERT have demonstrated impressive gain in generalization for a variety of tasks, with further improvement from adversarial fine-tuning.
Ranked #6 on Natural Language Inference on ANLI test (using extra training data)
2 code implementations • EMNLP 2020 • Liyuan Liu, Xiaodong Liu, Jianfeng Gao, Weizhu Chen, Jiawei Han
Transformers have proved effective in many NLP tasks.
Ranked #5 on Machine Translation on WMT2014 English-French
4 code implementations • ECCV 2020 • Xiujun Li, Xi Yin, Chunyuan Li, Pengchuan Zhang, Xiao-Wei Hu, Lei Zhang, Lijuan Wang, Houdong Hu, Li Dong, Furu Wei, Yejin Choi, Jianfeng Gao
Large-scale pre-training methods of learning cross-modal representations on image-text pairs are becoming popular for vision-language tasks.
Ranked #1 on Image Retrieval on MS COCO (Recall@10 metric)
no code implementations • 9 Apr 2020 • Swadheen Shukla, Lars Liden, Shahin Shayandeh, Eslam Kamal, Jinchao Li, Matt Mazzola, Thomas Park, Baolin Peng, Jianfeng Gao
Traditionally, industry solutions for building a task-oriented dialog system have relied on helping dialog authors define rule-based dialog managers, represented as dialog flows.
1 code implementation • 7 Apr 2020 • Ziming Li, Sungjin Lee, Baolin Peng, Jinchao Li, Julia Kiseleva, Maarten de Rijke, Shahin Shayandeh, Jianfeng Gao
Reinforcement Learning (RL) methods have emerged as a popular choice for training an efficient and effective dialogue policy.
2 code implementations • 6 Apr 2020 • Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, Jianfeng Gao
Deep learning based models have surpassed classical machine learning based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference.
2 code implementations • ICML 2020 • Yang Zhao, Chunyuan Li, Ping Yu, Jianfeng Gao, Changyou Chen
The instability in GAN training has been a long-standing problem despite remarkable research efforts.
Ranked #1 on Conditional Image Generation on CIFAR-100
1 code implementation • EMNLP 2020 • Chunyuan Li, Xiang Gao, Yuan Li, Baolin Peng, Xiujun Li, Yizhe Zhang, Jianfeng Gao
We hope that our first pre-trained big VAE language model itself and results can help the NLP community renew the interests of deep generative models in the era of large-scale pre-training, and make these principled methods more practical.
no code implementations • 2 Mar 2020 • Qiaolin Xia, Xiujun Li, Chunyuan Li, Yonatan Bisk, Zhifang Sui, Jianfeng Gao, Yejin Choi, Noah A. Smith
Learning to navigate in a visual environment following natural language instructions is a challenging task because natural language instructions are highly variable, ambiguous, and under-specified.
3 code implementations • 28 Feb 2020 • Hangbo Bao, Li Dong, Furu Wei, Wenhui Wang, Nan Yang, Xiaodong Liu, Yu Wang, Songhao Piao, Jianfeng Gao, Ming Zhou, Hsiao-Wuen Hon
We propose to pre-train a unified language model for both autoencoding and partially autoregressive language modeling tasks using a novel training procedure, referred to as a pseudo-masked language model (PMLM).
Ranked #4 on Question Generation on SQuAD1.1 (using extra training data)
2 code implementations • Findings of the Association for Computational Linguistics 2020 • Baolin Peng, Chenguang Zhu, Chunyuan Li, Xiujun Li, Jinchao Li, Michael Zeng, Jianfeng Gao
It is pre-trained on a large set of annotated NLG corpus to acquire the controllable generation ability, and fine-tuned with only a few domain-specific labels to adapt to new domains.
Ranked #4 on Data-to-Text Generation on MULTIWOZ 2.1
1 code implementation • CVPR 2020 • Weituo Hao, Chunyuan Li, Xiujun Li, Lawrence Carin, Jianfeng Gao
By training on a large amount of image-text-action triplets in a self-supervised learning manner, the pre-trained model provides generic representations of visual environments and language instructions.
Ranked #1 on Visual Navigation on Help, Anna! (HANNA)
3 code implementations • ACL 2020 • Xiaodong Liu, Yu Wang, Jianshu ji, Hao Cheng, Xueyun Zhu, Emmanuel Awa, Pengcheng He, Weizhu Chen, Hoifung Poon, Guihong Cao, Jianfeng Gao
We present MT-DNN, an open-source natural language understanding (NLU) toolkit that makes it easy for researchers and developers to train customized deep learning models.
1 code implementation • ACL 2020 • Qi Zhu, Zheng Zhang, Yan Fang, Xiang Li, Ryuichi Takanobu, Jinchao Li, Baolin Peng, Jianfeng Gao, Xiaoyan Zhu, Minlie Huang
We present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems.
2 code implementations • 26 Nov 2019 • Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, Yejin Choi
Questions requiring this kind of physical commonsense pose a challenge to today's natural language understanding systems.
Ranked #36 on Question Answering on PIQA
Natural Language Understanding Physical Commonsense Reasoning +1
no code implementations • 14 Nov 2019 • Seokhwan Kim, Michel Galley, Chulaka Gunasekara, Sungjin Lee, Adam Atkinson, Baolin Peng, Hannes Schulz, Jianfeng Gao, Jinchao Li, Mahmoud Adada, Minlie Huang, Luis Lastras, Jonathan K. Kummerfeld, Walter S. Lasecki, Chiori Hori, Anoop Cherian, Tim K. Marks, Abhinav Rastogi, Xiaoxue Zang, Srinivas Sunkara, Raghav Gupta
This paper introduces the Eighth Dialog System Technology Challenge.
6 code implementations • ACL 2020 • Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, Tuo Zhao
However, due to limited data resources from downstream tasks and the extremely large capacity of pre-trained models, aggressive fine-tuning often causes the adapted model to overfit the data of downstream tasks and forget the knowledge of the pre-trained model.
Ranked #1 on Natural Language Inference on QNLI
1 code implementation • EACL 2021 • Woon Sang Cho, Yizhe Zhang, Sudha Rao, Asli Celikyilmaz, Chenyan Xiong, Jianfeng Gao, Mengdi Wang, Bill Dolan
In the SL stage, a single-document question generator is trained.
6 code implementations • 1 Nov 2019 • Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan
We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer).
1 code implementation • 25 Oct 2019 • Mehrad Moradshahi, Hamid Palangi, Monica S. Lam, Paul Smolensky, Jianfeng Gao
We introduce HUBERT which combines the structured-representational power of Tensor-Product Representations (TPRs) and BERT, a pre-trained bidirectional Transformer language model.