no code implementations • EMNLP 2020 • Bin Bi, Chenliang Li, Chen Wu, Ming Yan, Wei Wang, Songfang Huang, Fei Huang, Luo Si
An extensive set of experiments show that PALM achieves new state-of-the-art results on a variety of language generation benchmarks covering generative question answering (Rank 1 on the official MARCO leaderboard), abstractive summarization on CNN/DailyMail as well as Gigaword, question generation on SQuAD, and conversational response generation on Cornell Movie Dialogues.
Abstractive Text Summarization
Conversational Response Generation
+9
no code implementations • COLING 2022 • Yan Li, Chenliang Li, Junjun Guo
Asymmetric text matching has becoming increasingly indispensable for many downstream tasks (e. g., IR and NLP).
no code implementations • 22 Mar 2025 • Ruijia Zhang, Siliang Zeng, Chenliang Li, Alfredo Garcia, Mingyi Hong
To address this problem, we propose the first two-timescale single-loop IRL algorithm under neural network parameterized reward and provide a non-asymptotic convergence analysis under overparameterization.
1 code implementation • 7 Mar 2025 • Xuenan Xu, Jiahao Mei, Chenliang Li, Yuning Wu, Ming Yan, Shaopeng Lai, Ji Zhang, Mengyue Wu
The rapid advancement of large language models (LLMs) and artificial intelligence-generated content (AIGC) has accelerated AI-native applications, such as AI-based storybooks that automate engaging story production for children.
1 code implementation • 7 Mar 2025 • Yuning Wu, Jiahao Mei, Ming Yan, Chenliang Li, Shaopeng Lai, Yuran Ren, Zijia Wang, Ji Zhang, Mengyue Wu, Qin Jin, Fei Huang
Recent advancements in large language models (LLMs) have significantly enhanced text generation capabilities, yet evaluating their performance in generative writing remains a challenge.
no code implementations • 9 Aug 2024 • Tianyuan Shi, Fanqi Wan, Canbin Huang, Xiaojun Quan, Chenliang Li, Ming Yan, Ji Zhang
While fusing the capacities and advantages of various large language models (LLMs) offers a pathway to construct more powerful and versatile models, a fundamental challenge is to properly select advantageous model during the training.
no code implementations • 2 Jul 2024 • Chaoran Zhang, Lixin Zou, Dan Luo, Min Tang, Xiangyang Luo, Zihao Li, Chenliang Li
In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide array of text-centric tasks.
no code implementations • 11 Jun 2024 • Chenliang Li, Siliang Zeng, Zeyi Liao, Jiaxiang Li, Dongyeop Kang, Alfredo Garcia, Mingyi Hong
Aligning human preference and value is an important requirement for building contemporary foundation models and embodied AI.
1 code implementation • 28 May 2024 • Jiaxiang Li, Siliang Zeng, Hoi-To Wai, Chenliang Li, Alfredo Garcia, Mingyi Hong
State-of-the-art techniques such as Reinforcement Learning from Human Feedback (RLHF) often consist of two stages: 1) supervised fine-tuning (SFT), where the model is fine-tuned by learning from human demonstration data; 2) Preference learning, where preference data is used to learn a reward model, which is in turn used by a reinforcement learning (RL) step to fine-tune the model.
2 code implementations • 20 Mar 2024 • Hongzhan Chen, Hehong Chen, Ming Yan, Wenshen Xu, Xing Gao, Weizhou Shen, Xiaojun Quan, Chenliang Li, Ji Zhang, Fei Huang, Jingren Zhou
In this paper, we introduce SocialBench, the first benchmark designed to systematically evaluate the sociality of role-playing conversational agents at both individual and group levels of social interactions.
no code implementations • 1 Mar 2024 • Haowei Liu, Yaya Shi, Haiyang Xu, Chunfeng Yuan, Qinghao Ye, Chenliang Li, Ming Yan, Ji Zhang, Fei Huang, Bing Li, Weiming Hu
In vision-language pre-training (VLP), masked image modeling (MIM) has recently been introduced for fine-grained cross-modal alignment.
no code implementations • 27 Feb 2024 • Xiaokun Zhang, Bo Xu, Chenliang Li, Yao Zhou, Liangyue Li, Hongfei Lin
Emerging efforts incorporate various kinds of side information into their methods for enhancing task performance.
no code implementations • 26 Feb 2024 • Haowei Liu, Yaya Shi, Haiyang Xu, Chunfeng Yuan, Qinghao Ye, Chenliang Li, Ming Yan, Ji Zhang, Fei Huang, Bing Li, Weiming Hu
In this work, we propose the UNIFY framework, which learns lexicon representations to capture fine-grained semantics and combines the strengths of latent and lexicon representations for video-text retrieval.
1 code implementation • 14 Jan 2024 • Weizhou Shen, Chenliang Li, Hongzhan Chen, Ming Yan, Xiaojun Quan, Hehong Chen, Ji Zhang, Fei Huang
Each component is implemented by a single LLM that focuses on a specific capability and collaborates with others to accomplish the task.
no code implementations • 13 Jan 2024 • Hongzhan Chen, Ruijun Chen, Yuqi Yi, Xiaojun Quan, Chenliang Li, Ming Yan, Ji Zhang
Given the exceptional performance of proprietary large language models (LLMs) like GPT-4, recent research has increasingly focused on boosting the capabilities of smaller models through knowledge distillation (KD) from these powerful yet black-box teachers.
no code implementations • 11 Jan 2024 • Wei Ye, Chaoya Jiang, Haiyang Xu, Chenhao Ye, Chenliang Li, Ming Yan, Shikun Zhang, Songhang Huang, Fei Huang
Vision Transformers (ViTs) have become increasingly popular in large-scale Vision and Language Pre-training (VLP) models.
1 code implementation • 30 Nov 2023 • Anwen Hu, Yaya Shi, Haiyang Xu, Jiabo Ye, Qinghao Ye, Ming Yan, Chenliang Li, Qi Qian, Ji Zhang, Fei Huang
In this work, towards a more versatile copilot for academic paper writing, we mainly focus on strengthening the multi-modal diagram analysis ability of Multimodal LLMs.
1 code implementation • 2 Nov 2023 • Xiaokun Zhang, Bo Xu, Fenglong Ma, Chenliang Li, Yuan Lin, Hongfei Lin
Secondly, price preference and interest preference are interdependent and collectively determine user choice, necessitating that we jointly consider both price and interest preference for intent modeling.
no code implementations • 1 Nov 2023 • You Zhou, Xiujing Lin, Xiang Zhang, Maolin Wang, Gangwei Jiang, Huakang Lu, Yupeng Wu, Kai Zhang, Zhe Yang, Kehang Wang, Yongduo Sui, Fengwei Jia, Zuoli Tang, Yao Zhao, Hongxuan Zhang, Tiannuo Yang, Weibo Chen, Yunong Mao, Yi Li, De Bao, Yu Li, Hongrui Liao, Ting Liu, Jingwen Liu, Jinchi Guo, Xiangyu Zhao, Ying WEI, Hong Qian, Qi Liu, Xiang Wang, Wai Kin, Chan, Chenliang Li, Yusen Li, Shiyu Yang, Jining Yan, Chao Mou, Shuai Han, Wuxia Jin, Guannan Zhang, Xiaodong Zeng
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
no code implementations • 22 Oct 2023 • Zuoli Tang, ZhaoXin Huan, Zihao Li, Xiaolu Zhang, Jun Hu, Chilin Fu, Jun Zhou, Chenliang Li
We expect that by mixing the user's behaviors across different domains, we can exploit the common knowledge encoded in the pre-trained language model to alleviate the problems of data sparsity and cold start problems.
1 code implementation • 8 Oct 2023 • Jiabo Ye, Anwen Hu, Haiyang Xu, Qinghao Ye, Ming Yan, Guohai Xu, Chenliang Li, Junfeng Tian, Qi Qian, Ji Zhang, Qin Jin, Liang He, Xin Alex Lin, Fei Huang
Text is ubiquitous in our visual world, conveying crucial information, such as in documents, websites, and everyday photographs.
1 code implementation • 29 Sep 2023 • Xiaokun Zhang, Bo Xu, Fenglong Ma, Chenliang Li, Liang Yang, Hongfei Lin
(2) How to fuse these heterogeneous descriptive information to comprehensively infer user interests?
2 code implementations • 15 Sep 2023 • Ran Wei, Siliang Zeng, Chenliang Li, Alfredo Garcia, Anthony McDonald, Mingyi Hong
We consider a Bayesian approach to offline model-based inverse reinforcement learning (IRL).
3 code implementations • 2 Sep 2023 • Chenliang Li, Hehong Chen, Ming Yan, Weizhou Shen, Haiyang Xu, Zhikai Wu, Zhicheng Zhang, Wenmeng Zhou, Yingda Chen, Chen Cheng, Hongzhu Shi, Ji Zhang, Fei Huang, Jingren Zhou
Large language models (LLMs) have recently demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior.
no code implementations • 19 Jul 2023 • Qingyao Ai, Ting Bai, Zhao Cao, Yi Chang, Jiawei Chen, Zhumin Chen, Zhiyong Cheng, Shoubin Dong, Zhicheng Dou, Fuli Feng, Shen Gao, Jiafeng Guo, Xiangnan He, Yanyan Lan, Chenliang Li, Yiqun Liu, Ziyu Lyu, Weizhi Ma, Jun Ma, Zhaochun Ren, Pengjie Ren, Zhiqiang Wang, Mingwen Wang, Ji-Rong Wen, Le Wu, Xin Xin, Jun Xu, Dawei Yin, Peng Zhang, Fan Zhang, Weinan Zhang, Min Zhang, Xiaofei Zhu
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs.
no code implementations • 17 Jul 2023 • Chaoya Jiang, Haiyang Xu, Wei Ye, Qinghao Ye, Chenliang Li, Ming Yan, Bin Bi, Shikun Zhang, Fei Huang, Songfang Huang
Specifically, We incorporate a Text-Semantics-Aware Patch Selector (TSPS) into the ViT backbone to perform a coarse-grained visual token extraction and then attach a flexible Transformer-based Patch Abstraction Decoder (PAD) upon the backbone for top-level visual abstraction.
1 code implementation • 4 Jul 2023 • Jiabo Ye, Anwen Hu, Haiyang Xu, Qinghao Ye, Ming Yan, Yuhao Dan, Chenlin Zhao, Guohai Xu, Chenliang Li, Junfeng Tian, Qian Qi, Ji Zhang, Fei Huang
Nevertheless, without in-domain training, these models tend to ignore fine-grained OCR features, such as sophisticated tables or large blocks of text, which are essential for OCR-free document understanding.
no code implementations • 29 Jun 2023 • Yu Tian, Bofang Li, Si Chen, Xubin Li, Hongbo Deng, Jian Xu, Bo Zheng, Qian Wang, Chenliang Li
Recently, Multi-Scenario Learning (MSL) is widely used in recommendation and retrieval systems in the industry because it facilitates transfer learning from different scenarios, mitigating data sparsity and reducing maintenance cost.
1 code implementation • 7 Jun 2023 • Haiyang Xu, Qinghao Ye, Xuan Wu, Ming Yan, Yuan Miao, Jiabo Ye, Guohai Xu, Anwen Hu, Yaya Shi, Guangwei Xu, Chenliang Li, Qi Qian, Maofei Que, Ji Zhang, Xiao Zeng, Fei Huang
In addition, to facilitate a comprehensive evaluation of video-language models, we carefully build the largest human-annotated Chinese benchmarks covering three popular video-language tasks of cross-modal retrieval, video captioning, and video category classification.
no code implementations • 17 May 2023 • Dan Luo, Lixin Zou, Qingyao Ai, Zhiyu Chen, Chenliang Li, Dawei Yin, Brian D. Davison
The goal of unbiased learning to rank (ULTR) is to leverage implicit user feedback for optimizing learning-to-rank systems.
1 code implementation • 3 May 2023 • Xu Yang, Jiawei Peng, Zihua Wang, Haiyang Xu, Qinghao Ye, Chenliang Li, Songfang Huang, Fei Huang, Zhangzikang Li, Yu Zhang
In TSG, we apply multi-head attention (MHA) to design the Graph Neural Network (GNN) for embedding scene graphs.
1 code implementation • 27 Apr 2023 • Qinghao Ye, Haiyang Xu, Guohai Xu, Jiabo Ye, Ming Yan, Yiyang Zhou, Junyang Wang, Anwen Hu, Pengcheng Shi, Yaya Shi, Chenliang Li, Yuanhong Xu, Hehong Chen, Junfeng Tian, Qi Qian, Ji Zhang, Fei Huang, Jingren Zhou
Our code, pre-trained model, instruction-tuned models, and evaluation set are available at https://github. com/X-PLUG/mPLUG-Owl.
Ranked #1 on
Visual Question Answering (VQA)
on HallusionBench
(Question Pair Acc metric)
Visual Question Answering (VQA)
Zero-Shot Video Question Answer
no code implementations • 19 Apr 2023 • Hao Fei, Tat-Seng Chua, Chenliang Li, Donghong Ji, Meishan Zhang, Yafeng Ren
In this study, we propose to enhance the ABSA robustness by systematically rethinking the bottlenecks from all possible angles, including model, data, and training.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
1 code implementation • 16 Apr 2023 • Junfeng Tian, Hehong Chen, Guohai Xu, Ming Yan, Xing Gao, Jianhai Zhang, Chenliang Li, Jiayi Liu, Wenshen Xu, Haiyang Xu, Qi Qian, Wei Wang, Qinghao Ye, Jiejing Zhang, Ji Zhang, Fei Huang, Jingren Zhou
In this paper, we present ChatPLUG, a Chinese open-domain dialogue system for digital human applications that instruction finetunes on a wide range of dialogue tasks in a unified internet-augmented format.
1 code implementation • 3 Apr 2023 • Zihao Li, Aixin Sun, Chenliang Li
Mainstream solutions to Sequential Recommendation (SR) represent items with fixed vectors.
2 code implementations • NeurIPS 2023 • Siliang Zeng, Chenliang Li, Alfredo Garcia, Mingyi Hong
Offline inverse reinforcement learning (Offline IRL) aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent.
4 code implementations • 1 Feb 2023 • Haiyang Xu, Qinghao Ye, Ming Yan, Yaya Shi, Jiabo Ye, Yuanhong Xu, Chenliang Li, Bin Bi, Qi Qian, Wei Wang, Guohai Xu, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou
In contrast to predominant paradigms of solely relying on sequence-to-sequence generation or encoder-based instance discrimination, mPLUG-2 introduces a multi-module composition network by sharing common universal modules for modality collaboration and disentangling different modality modules to deal with modality entanglement.
Ranked #1 on
Video Captioning
on MSR-VTT
no code implementations • ICCV 2023 • Xu Yang, Zhangzikang Li, Haiyang Xu, Hanwang Zhang, Qinghao Ye, Chenliang Li, Ming Yan, Yu Zhang, Fei Huang, Songfang Huang
To amend this, we propose a novel TW-BERT to learn Trajectory-Word alignment by a newly designed trajectory-to-word (T2W) attention for solving video-language tasks.
no code implementations • ICCV 2023 • Chaoya Jiang, Haiyang Xu, Wei Ye, Qinghao Ye, Chenliang Li, Ming Yan, Bin Bi, Shikun Zhang, Fei Huang, Songfang Huang
In this paper, we propose a Bottom-Up Patch Summarization approach named BUS which is inspired by the Document Summarization Task in NLP to learn a concise visual summary of lengthy visual token sequences, guided by textual semantics.
no code implementations • 4 Oct 2022 • Siliang Zeng, Chenliang Li, Alfredo Garcia, Mingyi Hong
To reduce the computational burden of a nested loop, novel methods such as SQIL [1] and IQ-Learn [2] emphasize policy estimation at the expense of reward estimation accuracy.
1 code implementation • Conference 2022 • Hao Fei, Fei Li, Chenliang Li, Shengqiong Wu, Jingye Li, Donghong Ji
So far, aspect-based sentiment analysis (ABSA) has involved with total seven subtasks, in which, however the interactions among them have been left unexplored sufficiently.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+4
1 code implementation • Conference 2022 • Hao Fei, Jingye Li, Shengqiong Wu, Chenliang Li, Donghong Ji, Fei Li
In our global reasoning framework, D2G and ARG work collaboratively, iteratively performing lexical, syntactic and semantic information exchange and representation learning over the entire dialogue context.
Ranked #3 on
Dialog Relation Extraction
on DialogRE
1 code implementation • 12 Jul 2022 • Yuhao Yang, Chao Huang, Lianghao Xia, Yuxuan Liang, Yanwei Yu, Chenliang Li
Further ablation studies validate the effectiveness of our model design and benefits of the new MBHT framework.
no code implementations • 27 Jun 2022 • Chuwei Luo, Guozhi Tang, Qi Zheng, Cong Yao, Lianwen Jin, Chenliang Li, Yang Xue, Luo Si
Multi-modal document pre-trained models have proven to be very effective in a variety of visually-rich document understanding (VrDU) tasks.
no code implementations • 13 Jun 2022 • Zitao Song, Xuyang Jin, Chenliang Li
In recent years, many practitioners in quantitative finance have attempted to use Deep Reinforcement Learning (DRL) to build better quantitative trading (QT) strategies.
no code implementations • 28 May 2022 • Xinyu Zou, Zhi Hu, Yiming Zhao, Xuchu Ding, Zhongyi Liu, Chenliang Li, Aixin Sun
At each multi-scenario/multi-task layer, a novel expert selection algorithm is proposed to automatically identify scenario-/task-specific and shared experts for each input.
3 code implementations • 24 May 2022 • Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, Hehong Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Luo Si
Large-scale pretrained foundation models have been an emerging paradigm for building artificial intelligence (AI) systems, which can be quickly adapted to a wide range of downstream tasks.
Ranked #1 on
Image Captioning
on COCO Captions
1 code implementation • 9 May 2022 • Xiaokun Zhang, Bo Xu, Liang Yang, Chenliang Li, Fenglong Ma, Haifeng Liu, Hongfei Lin
Finally, we predict user actions based on item features and users' price and interest preferences.
1 code implementation • 3 May 2022 • Yu Tian, Jianxin Chang, Yannan Niu, Yang song, Chenliang Li
Specifically, multi-interest methods such as ComiRec and MIMN, focus on extracting different interests for a user by performing historical item clustering, while graph convolution methods including TGSRec and SURGE elect to refine user preferences based on multi-level correlations between historical items.
1 code implementation • 2 May 2022 • Yuhao Yang, Chao Huang, Lianghao Xia, Chenliang Li
However, the success of such methods relies on the high quality knowledge graphs, and may not learn quality representations with two challenges: i) The long-tail distribution of entities results in sparse supervision signals for KG-enhanced item representation; ii) Real-world knowledge graphs are often noisy and contain topic-irrelevant connections between items and entities.
1 code implementation • 12 Apr 2022 • Yitong Ji, Aixin Sun, Jie Zhang, Chenliang Li
Our study offers a different perspective to understand recommender accuracy, and our findings could trigger a revisit of recommender model design.
no code implementations • 17 Nov 2021 • Ming Yan, Haiyang Xu, Chenliang Li, Junfeng Tian, Bin Bi, Wei Wang, Weihua Chen, Xianzhe Xu, Fan Wang, Zheng Cao, Zhicheng Zhang, Qiyu Zhang, Ji Zhang, Songfang Huang, Fei Huang, Luo Si, Rong Jin
The Visual Question Answering (VQA) task utilizes both visual image and language analysis to answer a textual question with respect to an image.
Ranked #8 on
Visual Question Answering (VQA)
on VQA v2 test-dev
no code implementations • 28 Sep 2021 • Yiyu Liu, Qian Liu, Yu Tian, Changping Wang, Yanan Niu, Yang song, Chenliang Li
In this paper, we propose a novel concept-aware denoising graph neural network (named CONDE) for micro-video recommendation.
no code implementations • 21 Aug 2021 • Ming Yan, Haiyang Xu, Chenliang Li, Bin Bi, Junfeng Tian, Min Gui, Wei Wang
Existing approaches to vision-language pre-training (VLP) heavily rely on an object detector based on bounding boxes (regions), where salient objects are first detected from images and then a Transformer-based model is used for cross-modal fusion.
no code implementations • ACL 2021 • Chenliang Li, Bin Bi, Ming Yan, Wei Wang, Songfang Huang
This work focuses on generative QA which aims to generate an abstractive answer to a given question instead of extracting an answer span from a provided passage.
no code implementations • SEMEVAL 2021 • Junfeng Tian, Min Gui, Chenliang Li, Ming Yan, Wenming Xiao
We describe our systems of subtask1 and subtask3 for SemEval-2021 Task 6 on Detection of Persuasion Techniques in Texts and Images.
no code implementations • ACL 2021 • Haiyang Xu, Ming Yan, Chenliang Li, Bin Bi, Songfang Huang, Wenming Xiao, Fei Huang
Vision-language pre-training (VLP) on large-scale image-text pairs has achieved huge success for the cross-modal downstream tasks.
1 code implementation • ACL 2021 • Chenliang Li, Bin Bi, Ming Yan, Wei Wang, Songfang Huang, Fei Huang, Luo Si
Large pre-trained language models achieve state-of-the-art results when fine-tuned on downstream NLP tasks.
no code implementations • 18 May 2021 • Houyi Li, Zhihong Chen, Chenliang Li, Rong Xiao, Hongbo Deng, Peng Zhang, Yongchao Liu, Haihong Tang
PDN utilizes Trigger Net to capture the user's interest in each of his/her interacted item, and Similarity Net to evaluate the similarity between each interacted item and the target item based on these items' profile and CF information.
no code implementations • 14 Mar 2021 • Chenliang Li, Ming Yan, Haiyang Xu, Fuli Luo, Wei Wang, Bin Bi, Songfang Huang
Vision-language pre-training (VLP) on large-scale image-text pairs has recently witnessed rapid progress for learning cross-modal representations.
1 code implementation • Asian Chapter of the Association for Computational Linguistics 2020 • Canwen Xu, Tao Ge, Chenliang Li, Furu Wei
Chinese and Japanese share many characters with similar surface morphology.
1 code implementation • 21 Oct 2020 • Yitong Ji, Aixin Sun, Jie Zhang, Chenliang Li
To evaluate recommendation systems in a realistic manner in offline setting, we propose a timeline scheme, which calls for a revisit of the recommendation model design.
no code implementations • ACL 2020 • Hao Tang, Donghong Ji, Chenliang Li, Qiji Zhou
The idea is to allow the dependency graph to guide the representation learning of the transformer encoder and vice versa.
1 code implementation • 28 May 2020 • Yitong Ji, Aixin Sun, Jie Zhang, Chenliang Li
On the widely used MovieLens dataset, we show that the performance of popularity could be significantly improved by 70% or more, if we consider the popular items at the time point when a user interacts with the system.
1 code implementation • 21 May 2020 • Zhihong Chen, Rong Xiao, Chenliang Li, Gangfeng Ye, Haochuan Sun, Hongbo Deng
Most of ranking models are trained only with displayed items (most are hot items), but they are utilized to retrieve items in the entire space which consists of both displayed and non-displayed items (most are long-tail items).
1 code implementation • 21 May 2020 • Cheng Zhao, Chenliang Li, Rong Xiao, Hongbo Deng, Aixin Sun
Given two relevant domains (e. g., Book and Movie), users may have interactions with items in one domain but not in the other domain.
1 code implementation • ACL 2020 • Canwen Xu, Jiaxin Pei, Hongtao Wu, Yiyu Liu, Chenliang Li
Recently, large-scale datasets have vastly facilitated the development in nearly all domains of Natural Language Processing.
2 code implementations • 14 Apr 2020 • Bin Bi, Chenliang Li, Chen Wu, Ming Yan, Wei Wang, Songfang Huang, Fei Huang, Luo Si
An extensive set of experiments show that PALM achieves new state-of-the-art results on a variety of language generation benchmarks covering generative question answering (Rank 1 on the official MARCO leaderboard), abstractive summarization on CNN/DailyMail as well as Gigaword, question generation on SQuAD, and conversational response generation on Cornell Movie Dialogues.
Ranked #1 on
Text Generation
on CNN/Daily Mail
Abstractive Text Summarization
Conversational Response Generation
+9
no code implementations • 31 Dec 2019 • Jialong Han, Aixin Sun, Haisong Zhang, Chenliang Li, Shuming Shi
In this study, we demonstrate that annotations for this task can be harvested at scale from existing corpora, in a fully automatic manner.
no code implementations • Conference 2019 • Cheng Zhao, Chenliang Li, Cong Fu
We find there are mainly three problems in their formulations: 1) their knowledge transfer is unaware of the cross-domain graph structure.
1 code implementation • ACL 2020 • Yu Duan, Canwen Xu, Jiaxin Pei, Jialong Han, Chenliang Li
Conditional Text Generation has drawn much attention as a topic of Natural Language Generation (NLG) which provides the possibility for humans to control the properties of generated contents.
no code implementations • IJCNLP 2019 • Bin Bi, Chen Wu, Ming Yan, Wei Wang, Jiangnan Xia, Chenliang Li
Different from existing work on knowledge-aware QA, we focus on a more challenging task of leveraging external knowledge to generate answers in natural language for a given question with context.
1 code implementation • 28 Aug 2019 • Canwen Xu, Feiyang Wang, Jialong Han, Chenliang Li
Identifying the named entities mentioned in text would enrich many semantic applications at the downstream level.
Chinese Named Entity Recognition
named-entity-recognition
+2
no code implementations • 2 Jul 2019 • Canwen Xu, Zhenzhong Chen, Chenliang Li
Recently, with the prevalence of large-scale image dataset, the co-occurrence information among classes becomes rich, calling for a new way to exploit it to facilitate inference.
1 code implementation • 1 Jul 2019 • Chenliang Li, Cong Quan, Li Peng, Yunwei Qi, Yuming Deng, Libing Wu
A sentiment capsule architecture with a novel Routing by Bi-Agreement mechanism is proposed to identify the informative logic unit and the sentiment based representations in user-item level for rating prediction.
no code implementations • 1 Jul 2019 • Chenliang Li, Xichuan Niu, Xiangyang Luo, Zhenzhong Chen, Cong Quan
Given a sequence of historical purchased items for a user, we devise a novel hierarchical attention over attention mechanism to capture sequential patterns at both union-level and individual-level.
1 code implementation • 9 May 2019 • Jiaxin Pei, Aixin Sun, Chenliang Li
Targeted sentiment analysis (TSA), also known as aspect based sentiment analysis (ABSA), aims at detecting fine-grained sentiment polarity towards targets in a given opinion document.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
1 code implementation • 27 Apr 2019 • Shiqian Chen, Chenliang Li, Feng Ji, Wei Zhou, Haiqing Chen
Then, we devise a mechanism to identify the relevant information from the noise-prone review snippets and incorporate this information to guide the answer generation.
1 code implementation • 21 Jan 2019 • Wei Emma Zhang, Quan Z. Sheng, Ahoud Alhazmi, Chenliang Li
In this article, we review research works that address this difference and generatetextual adversarial examples on DNNs.
no code implementations • 21 Jan 2019 • Canwen Xu, Jing Li, Xiangyang Luo, Jiaxin Pei, Chenliang Li, Donghong Ji
Recognizing and linking such fine-grained location mentions to well-defined location profiles are beneficial for retrieval and recommendation systems.
1 code implementation • 22 Dec 2018 • Jing Li, Aixin Sun, Jianglei Han, Chenliang Li
In this paper, we provide a comprehensive review on existing deep learning techniques for NER.
no code implementations • 22 Dec 2018 • Chenliang Li, Yu Duan, Haoran Wang, Zhiqian Zhang, Aixin Sun, Zongyang Ma
Recent studies show that the Dirichlet Multinomial Mixture (DMM) model is effective for topic inference over short texts by assuming that each piece of short text is generated by a single topic.
1 code implementation • 4 Nov 2018 • Peifeng Wang, Jialong Han, Chenliang Li, Rong pan
Recent efforts on this issue suggest training a neighborhood aggregator in conjunction with the conventional entity and relation embeddings, which may help embed new entities inductively via their existing neighbors.
no code implementations • EMNLP 2018 • Jiaxin Pei, Chenliang Li
In this paper, we propose Sequence to Sequence with Prototype Memory Network (S2SPMN) to exploit the relevant information provided by the large dialogue corpus to enhance response generation.
1 code implementation • ACL 2018 • Chenliang Li, Wei Zhou, Feng Ji, Yu Duan, Haiqing Chen
In the era of big data, focused analysis for diverse topics with a short response time becomes an urgent demand.
no code implementations • NAACL 2018 • Chenliang Li, Weiran Xu, Si Li, Sheng Gao
Then, we introduce a Key Information Guide Network (KIGN), which encodes the keywords to the key information representation, to guide the process of generation.
Ranked #10 on
Text Summarization
on CNN / Daily Mail (Anonymized)
no code implementations • 4 Feb 2018 • Minh C. Phan, Aixin Sun, Yi Tay, Jialong Han, Chenliang Li
For the first time, we show that the semantic relationships between the mentioned entities are in fact less dense than expected.
1 code implementation • 5 Nov 2017 • Daochen Zha, Chenliang Li
With a few seed words relevant to each category, SMTM conducts multi-label classification for a collection of documents without any labeled document.
no code implementations • 15 Apr 2017 • Zhiqian Zhang, Chenliang Li, Zhiyong Wu, Aixin Sun, Dengpan Ye, Xiangyang Luo
Inspired by the recent success of neural networks in many areas, in this paper, we present a simple but effective neural network framework for next POI recommendation, named NEXT.