1 code implementation • NAACL (ACL) 2022 • Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji
We introduce RESIN-11, a new schema-guided event extraction&prediction framework that can be applied to a large variety of newsworthy scenarios.
no code implementations • EMNLP 2020 • Manling Li, Qi Zeng, Ying Lin, Kyunghyun Cho, Heng Ji, Jonathan May, Nathanael Chambers, Clare Voss
Event schemas can guide our understanding and ability to make predictions with respect to what might happen next.
no code implementations • NAACL 2022 • Xiaomeng Jin, Manling Li, Heng Ji
To induce event schemas from historical events, previous work uses an event-by-event scheme, ignoring the global structure of the entire schema graph.
no code implementations • NAACL 2022 • Sha Li, Mahdi Namazifar, Di Jin, Mohit Bansal, Heng Ji, Yang Liu, Dilek Hakkani-Tur
In this work, we propose to automatically convert the background knowledge documents into document semantic graphs and then perform knowledge selection over such graphs.
no code implementations • NAACL (ACL) 2022 • Muhao Chen, Lifu Huang, Manling Li, Ben Zhou, Heng Ji, Dan Roth
This tutorial targets researchers and practitioners who are interested in AI and ML technologies for structural information extraction (IE) from unstructured textual sources.
no code implementations • Findings (EMNLP) 2021 • Zixuan Zhang, Hongwei Wang, Han Zhao, Hanghang Tong, Heng Ji
Relations in most of the traditional knowledge graphs (KGs) only reflect static and factual connections, but fail to represent the dynamic activities and state changes about entities.
1 code implementation • EMNLP 2021 • Carl Edwards, ChengXiang Zhai, Heng Ji
Moreover, this can be viewed as an especially challenging cross-lingual retrieval problem by considering the molecules as a language with a very unique grammar.
Ranked #2 on Cross-Modal Retrieval on ChEBI-20
1 code implementation • NAACL (TextGraphs) 2021 • Qi Zeng, Manling Li, Tuan Lai, Heng Ji, Mohit Bansal, Hanghang Tong
Current methods for event representation ignore related events in a corpus-level global context.
1 code implementation • EMNLP (ACL) 2021 • Wenhao Yu, Meng Jiang, Zhiting Hu, Qingyun Wang, Heng Ji, Nazneen Rajani
Knowledge-enriched text generation poses unique challenges in modeling and learning, driving active research in several core directions, ranging from integrated modeling of neural representations and symbolic information in the sequential/hierarchical/graphical structures, learning without direct supervisions due to the cost of structured annotation, efficient optimization and inference with massive and global constraints, to language grounding on multiple modalities, and generative reasoning with implicit commonsense knowledge and background knowledge.
no code implementations • EMNLP 2020 • Lifu Huang, Heng Ji
We design a Semi-Supervised Vector Quantized Variational Autoencoder framework to automatically learn a discrete latent type representation for each seen and unseen type and optimize them using seen type event annotations.
1 code implementation • ACL 2022 • Manling Li, Revanth Gangi Reddy, Ziqi Wang, Yi-shyuan Chiang, Tuan Lai, Pengfei Yu, Zixuan Zhang, Heng Ji
To tackle the challenge of accurate and timely communication regarding the COVID-19 pandemic, we present a COVID-19 Claim Radar to automatically extract supporting and refuting claims on a daily basis.
no code implementations • COLING 2022 • Revanth Gangi Reddy, Vikas Yadav, Md Arafat Sultan, Martin Franz, Vittorio Castelli, Heng Ji, Avirup Sil
Research on neural IR has so far been focused primarily on standard supervised learning settings, where it outperforms traditional term matching baselines.
no code implementations • ACL (ECNLP) 2021 • Ying Lin, Han Wang, Jiangning Chen, Tong Wang, Yue Liu, Heng Ji, Yang Liu, Premkumar Natarajan
We first build a cross-source heterogeneous knowledge graph from customer purchase history and product knowledge graph to jointly learn customer and product embeddings.
1 code implementation • EMNLP 2021 • Haoyang Wen, Heng Ji
Event time is one of the most important features for event-event temporal relation extraction.
no code implementations • COLING 2022 • Revanth Gangi Reddy, Sai Chetan Chinthakindi, Yi R. Fung, Kevin Small, Heng Ji
In recent years, there has been an increasing interest in claim detection as an important building block for misinformation detection.
1 code implementation • EMNLP 2021 • Pengfei Yu, Heng Ji, Prem Natarajan
We focus on lifelong event detection as an exemplar case and propose a new problem formulation that is also generalizable to other IE tasks.
1 code implementation • EMNLP 2021 • Manling Li, Tengfei Ma, Mo Yu, Lingfei Wu, Tian Gao, Heng Ji, Kathleen McKeown
Timeline Summarization identifies major events from a news collection and describes them following temporal order, with key dates tagged.
no code implementations • CRAC (ACL) 2021 • Liming Wang, Shengyu Feng, Xudong Lin, Manling Li, Heng Ji, Shih-Fu Chang
Event coreference resolution is critical to understand events in the growing number of online news with multiple modalities including text, video, speech, etc.
1 code implementation • Findings (NAACL) 2022 • Qi Zeng, Qiusi Zhan, Heng Ji
Events are inter-related in documents.
1 code implementation • NAACL 2022 • Xueqing Wu, Kung-Hsiang Huang, Yi Fung, Heng Ji
Inspired by this process, we propose a novel task of cross-document misinformation detection.
no code implementations • 25 Apr 2024 • Chujie Zheng, Ziqi Wang, Heng Ji, Minlie Huang, Nanyun Peng
Suppose we have a moderately trained LLM (e. g., trained to align with human preference) in hand, can we further exploit its potential and cheaply acquire a stronger model?
no code implementations • 9 Apr 2024 • Zhenhailong Wang, Joy Hsu, Xingyao Wang, Kuan-Hao Huang, Manling Li, Jiajun Wu, Heng Ji
By casting an image to a text-based representation, we can leverage the power of language models to learn alignment from SVG to visual primitives and generalize to unseen question-answering tasks.
no code implementations • 2 Apr 2024 • Zixuan Zhang, Revanth Gangi Reddy, Kevin Small, Tong Zhang, Heng Ji
In addition, it is still unclear how well an OpenQA model can transfer to completely new knowledge domains.
1 code implementation • 27 Mar 2024 • Payam Karisani, Heng Ji
We then focus on the reader component and propose to train it such that it is insensitive towards the order of claims and evidence documents.
1 code implementation • 18 Mar 2024 • Kung-Hsiang Huang, Hou Pong Chan, Yi R. Fung, Haoyi Qiu, Mingyang Zhou, Shafiq Joty, Shih-Fu Chang, Heng Ji
This survey paper serves as a comprehensive resource for researchers and practitioners in the fields of natural language processing, computer vision, and data analysis, providing valuable insights and directions for future research in chart understanding leveraging large foundation models.
no code implementations • 26 Feb 2024 • Jeonghwan Kim, Heng Ji
Recent advances in instruction-tuned Large Vision-Language Models (LVLMs) have imbued the models with the ability to generate high-level, image-grounded explanations with ease.
1 code implementation • 22 Feb 2024 • Carl Edwards, Qingyun Wang, Lawrence Zhao, Heng Ji
Language-molecule models have emerged as an exciting direction for molecular discovery and understanding.
no code implementations • 19 Feb 2024 • Keyang Xuan, Li Yi, Fan Yang, Ruochen Wu, Yi R. Fung, Heng Ji
In this paper, we first investigate the potential of LVLM on multimodal misinformation detection.
no code implementations • 17 Feb 2024 • Jiateng Liu, Pengfei Yu, Yuji Zhang, Sha Li, Zixuan Zhang, Heng Ji
The dynamic nature of real-world information necessitates efficient knowledge editing (KE) in large language models (LLMs) for knowledge updating.
no code implementations • 16 Feb 2024 • Chenkai Sun, Ke Yang, Revanth Gangi Reddy, Yi R. Fung, Hou Pong Chan, ChengXiang Zhai, Heng Ji
The increasing demand for personalized interactions with large language models (LLMs) calls for the development of methodologies capable of accurately and efficiently identifying user opinions and preferences.
1 code implementation • 15 Feb 2024 • Henry W. Sprueill, Carl Edwards, Khushbu Agarwal, Mariefel V. Olarte, Udishnu Sanyal, Conrad Johnston, Hongbin Liu, Heng Ji, Sutanay Choudhury
The discovery of new catalysts is essential for the design of new and more efficient chemical processes in order to transition to a sustainable future.
1 code implementation • 14 Feb 2024 • Yi Fung, Ruining Zhao, Jae Doo, Chenkai Sun, Heng Ji
Pretrained large language models have revolutionized many applications but still face challenges related to cultural bias and a lack of cultural commonsense knowledge crucial for guiding cross-culture communication and interactions.
no code implementations • 12 Feb 2024 • Kyungha Kim, Sangyun Lee, Kung-Hsiang Huang, Hou Pong Chan, Manling Li, Heng Ji
Fact-checking research has extensively explored verification but less so the generation of natural-language explanations, crucial for user trust.
no code implementations • 9 Feb 2024 • Amin Karimi Monsefi, Payam Karisani, Mengxi Zhou, Stacey Choi, Nathan Doble, Heng Ji, Srinivasan Parthasarathy, Rajiv Ramnath
In this paper, we introduce a new neural network architecture, termed LoGoNet, with a tailored self-supervised learning (SSL) method to mitigate such challenges.
1 code implementation • 1 Feb 2024 • Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, Heng Ji
LLM agents are typically prompted to produce actions by generating JSON or text in a pre-defined format, which is usually limited by constrained action space (e. g., the scope of pre-defined tools) and restricted flexibility (e. g., inability to compose multiple tools).
1 code implementation • 19 Jan 2024 • Hongyi Liu, Qingyun Wang, Payam Karisani, Heng Ji
In our experiments, we observed that such a model is prone to mislabeling the source entities, which can often appear in the text, as the target entities.
1 code implementation • 18 Jan 2024 • Qingyun Wang, Zixuan Zhang, Hongxiang Li, Xuan Liu, Jiawei Han, Huimin Zhao, Heng Ji
Fine-grained few-shot entity extraction in the chemical domain faces two unique challenges.
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.
no code implementations • 1 Jan 2024 • Ke Yang, Jiateng Liu, John Wu, Chaoqi Yang, Yi R. Fung, Sha Li, Zixuan Huang, Xu Cao, Xingyao Wang, Yiquan Wang, Heng Ji, ChengXiang Zhai
The prominent large language models (LLMs) of today differ from past language models not only in size, but also in the fact that they are trained on a combination of natural language and formal language (code).
no code implementations • 18 Dec 2023 • Wei Xiong, Hanze Dong, Chenlu Ye, Ziqi Wang, Han Zhong, Heng Ji, Nan Jiang, Tong Zhang
This includes an iterative version of the Direct Preference Optimization (DPO) algorithm for online settings, and a multi-step rejection sampling strategy for offline scenarios.
2 code implementations • 15 Dec 2023 • Kung-Hsiang Huang, Mingyang Zhou, Hou Pong Chan, Yi R. Fung, Zhenhailong Wang, Lingyu Zhang, Shih-Fu Chang, Heng Ji
This work inaugurates a new domain in factual error correction for chart captions, presenting a novel evaluation mechanism, and demonstrating an effective approach to ensuring the factuality of generated chart captions.
Factual Inconsistency Detection in Chart Captioning Image Captioning +1
1 code implementation • 5 Dec 2023 • Khanh Duy Nguyen, Zixuan Zhang, Reece Suchocki, Sha Li, Martha Palmer, Susan Brown, Jiawei Han, Heng Ji
In this paper, we present RESIN-EDITOR, an interactive event graph visualizer and editor designed for analyzing complex events.
1 code implementation • 5 Dec 2023 • Bowen Jin, Gang Liu, Chi Han, Meng Jiang, Heng Ji, Jiawei Han
Besides, although LLMs have shown their pure text-based reasoning ability, it is underexplored whether such ability can be generalized to graphs (i. e., graph-based reasoning).
no code implementations • 27 Nov 2023 • Chi Han, Jialiang Xu, Manling Li, Hanning Zhang, Tarek Abdelzaher, Heng Ji
Social media play a significant role in shaping public opinion and influencing ideological communities through information propagation.
1 code implementation • 22 Nov 2023 • Yangyi Chen, Xingyao Wang, Manling Li, Derek Hoiem, Heng Ji
We adopt a weakly-supervised approach to directly generate visual event structures from captions for ViStruct training, capitalizing on abundant image-caption pairs from the web.
1 code implementation • 16 Nov 2023 • Kuan-Hao Huang, I-Hung Hsu, Tanmay Parekh, Zhiyu Xie, Zixuan Zhang, Premkumar Natarajan, Kai-Wei Chang, Nanyun Peng, Heng Ji
In this work, we identify and address evaluation challenges, including inconsistency due to varying data assumptions or preprocessing steps, the insufficiency of current evaluation frameworks that may introduce dataset or data split bias, and the low reproducibility of some previous approaches.
1 code implementation • 16 Nov 2023 • Hanning Zhang, Shizhe Diao, Yong Lin, Yi R. Fung, Qing Lian, Xingyao Wang, Yangyi Chen, Heng Ji, Tong Zhang
This approach is formalized by first identifying the knowledge gap between parametric knowledge and the instruction tuning data.
no code implementations • 16 Nov 2023 • Yangyi Chen, Karan Sikka, Michael Cogswell, Heng Ji, Ajay Divakaran
The critique NLF identifies the strengths and weaknesses of the responses and is used to align the LVLMs with human preferences.
no code implementations • 31 Oct 2023 • Sha Li, Chi Han, Pengfei Yu, Carl Edwards, Manling Li, Xingyao Wang, Yi R. Fung, Charles Yu, Joel R. Tetreault, Eduard H. Hovy, Heng Ji
The recent explosion of performance of large language models (LLMs) has changed the field of Natural Language Processing (NLP) more abruptly and seismically than any other shift in the field's 80-year history.
1 code implementation • 24 Oct 2023 • Yizhu Jiao, Ming Zhong, Sha Li, Ruining Zhao, Siru Ouyang, Heng Ji, Jiawei Han
However, when it comes to information extraction - a classic task in natural language processing - most task-specific systems cannot align well with long-tail ad hoc extraction use cases for non-expert users.
1 code implementation • 22 Oct 2023 • Henry W. Sprueill, Carl Edwards, Mariefel V. Olarte, Udishnu Sanyal, Heng Ji, Sutanay Choudhury
Discovering novel catalysts requires complex reasoning involving multiple chemical properties and resultant trade-offs, leading to a combinatorial growth in the search space.
no code implementations • 22 Oct 2023 • Revanth Gangi Reddy, Hao Bai, Wentao Yao, Sharath Chandra Etagi Suresh, Heng Ji, ChengXiang Zhai
Open-domain dialog involves generating search queries that help obtain relevant knowledge for holding informative conversations.
1 code implementation • 20 Oct 2023 • Chenkai Sun, Jinning Li, Yi R. Fung, Hou Pong Chan, Tarek Abdelzaher, ChengXiang Zhai, Heng Ji
Automatic response forecasting for news media plays a crucial role in enabling content producers to efficiently predict the impact of news releases and prevent unexpected negative outcomes such as social conflict and moral injury.
1 code implementation • 19 Oct 2023 • Siru Ouyang, Shuohang Wang, Yang Liu, Ming Zhong, Yizhu Jiao, Dan Iter, Reid Pryzant, Chenguang Zhu, Heng Ji, Jiawei Han
Recent progress in Large Language Models (LLMs) has produced models that exhibit remarkable performance across a variety of NLP tasks.
no code implementations • 11 Oct 2023 • Sumuk Shashidhar, Abhinav Chinta, Vaibhav Sahai, Zhenhailong Wang, Heng Ji
The dominance of proprietary LLMs has led to restricted access and raised information privacy concerns.
no code implementations • 8 Oct 2023 • Isabelle Augenstein, Timothy Baldwin, Meeyoung Cha, Tanmoy Chakraborty, Giovanni Luca Ciampaglia, David Corney, Renee DiResta, Emilio Ferrara, Scott Hale, Alon Halevy, Eduard Hovy, Heng Ji, Filippo Menczer, Ruben Miguez, Preslav Nakov, Dietram Scheufele, Shivam Sharma, Giovanni Zagni
The emergence of tools based on Large Language Models (LLMs), such as OpenAI's ChatGPT, Microsoft's Bing Chat, and Google's Bard, has garnered immense public attention.
no code implementations • 2 Oct 2023 • Ziqi Wang, Le Hou, Tianjian Lu, Yuexin Wu, Yunxuan Li, Hongkun Yu, Heng Ji
Specifically, we reformulate the training objective of reinforcement learning from human feedback (RLHF) -- instead of maximizing response quality for a given input, we maximize the quality gap of the response conditioned on a reference response.
no code implementations • 1 Oct 2023 • Tianci Xue, Ziqi Wang, Heng Ji
To this end, prior works incorporate controllable generations for alignment to make language models learn multiple preferences and provide outputs with different preferences during inference if asked.
1 code implementation • 29 Sep 2023 • Lifan Yuan, Yangyi Chen, Xingyao Wang, Yi R. Fung, Hao Peng, Heng Ji
It creates toolsets specifically curated for the tasks and equips LLMs with a component that retrieves tools from these sets to enhance their capability to solve complex tasks.
1 code implementation • 19 Sep 2023 • Xingyao Wang, Zihan Wang, Jiateng Liu, Yangyi Chen, Lifan Yuan, Hao Peng, Heng Ji
However, current evaluation protocols often emphasize benchmark performance with single-turn exchanges, neglecting the nuanced interactions among the user, LLMs, and external tools, while also underestimating the importance of natural language feedback from users.
no code implementations • 12 Sep 2023 • Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Heng Ji, Yuan YAO, Tong Zhang
Building on the analysis and the observation that averaging different layers of the transformer leads to significantly different reward-tax trade-offs, we propose Adaptive Model Averaging (AMA) to adaptively find various combination ratios of model layers.
1 code implementation • 8 Sep 2023 • Yangyi Chen, Karan Sikka, Michael Cogswell, Heng Ji, Ajay Divakaran
Based on this pipeline and the existing coarse-grained annotated dataset, we build the CURE benchmark to measure both the zero-shot reasoning performance and consistency of VLMs.
1 code implementation • 30 Aug 2023 • Chi Han, Qifan Wang, Hao Peng, Wenhan Xiong, Yu Chen, Heng Ji, Sinong Wang
As a result, their performance suffers drastically on inputs longer than those encountered during training, substantially limiting their applications in real-world tasks involving long contexts such as encoding scientific articles, code repositories, or long dialogues.
1 code implementation • 21 Jul 2023 • Yangyi Chen, Xingyao Wang, Heng Ji
In this work, we consider the practical scenario that we need to effectively utilize training samples to make PLMs both task-solvers and self-calibrators.
1 code implementation • 17 Jul 2023 • Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik Bekkers, Michael Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess Smidt, Shuiwang Ji
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
2 code implementations • 11 Jul 2023 • Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji
In this work, we propose Solo Performance Prompting (SPP), which transforms a single LLM into a cognitive synergist by engaging in multi-turn self-collaboration with multiple personas.
1 code implementation • 5 Jul 2023 • Sha Li, Ruining Zhao, Manling Li, Heng Ji, Chris Callison-Burch, Jiawei Han
Event schemas are a form of world knowledge about the typical progression of events.
1 code implementation • 27 Jun 2023 • Liliang Ren, Mankeerat Sidhu, Qi Zeng, Revanth Gangi Reddy, Heng Ji, ChengXiang Zhai
Existing reference-free turn-level evaluation metrics for chatbots inadequately capture the interaction between the user and the system.
no code implementations • 19 Jun 2023 • Carl Edwards, Aakanksha Naik, Tushar Khot, Martin Burke, Heng Ji, Tom Hope
We are given a small "personalized dataset" of 10-20 drug synergy relationships in the context of specific cancer cell targets.
1 code implementation • 7 Jun 2023 • Lifan Yuan, Yangyi Chen, Ganqu Cui, Hongcheng Gao, Fangyuan Zou, Xingyi Cheng, Heng Ji, Zhiyuan Liu, Maosong Sun
Then we introduce BOSS, a Benchmark suite for Out-of-distribution robustneSS evaluation covering 5 tasks and 20 datasets.
1 code implementation • 1 Jun 2023 • Xueqing Wu, Sha Li, Heng Ji
Open-vocabulary state tracking is a more practical version of state tracking that aims to track state changes of entities throughout a process without restricting the state space and entity space.
no code implementations • 29 May 2023 • Mingyang Zhou, Yi R. Fung, Long Chen, Christopher Thomas, Heng Ji, Shih-Fu Chang
Building cross-model intelligence that can understand charts and communicate the salient information hidden behind them is an appealing challenge in the vision and language(V+L) community.
1 code implementation • 29 May 2023 • Yangyi Chen, Hongcheng Gao, Ganqu Cui, Lifan Yuan, Dehan Kong, Hanlu Wu, Ning Shi, Bo Yuan, Longtao Huang, Hui Xue, Zhiyuan Liu, Maosong Sun, Heng Ji
In our experiments, we conduct a robustness evaluation of RoBERTa models to demonstrate the effectiveness of our evaluation framework, and further show the rationality of each component in the framework.
no code implementations • 29 May 2023 • Pengfei Yu, Heng Ji
To evaluate and address the core challenge, we propose a new task formulation of the information updating task that only requires the provision of an unstructured updating corpus and evaluates the performance of information updating on the generalizability to question-answer pairs pertaining to the updating information.
1 code implementation • 27 May 2023 • Zhenrui Yue, Huimin Zeng, Mengfei Lan, Heng Ji, Dong Wang
With emerging online topics as a source for numerous new events, detecting unseen / rare event types presents an elusive challenge for existing event detection methods, where only limited data access is provided for training.
1 code implementation • 27 May 2023 • Yu Zhou, Sha Li, Manling Li, Xudong Lin, Shih-Fu Chang, Mohit Bansal, Heng Ji
To automate the induction of such graph scripts for given tasks, we propose to take advantage of loosely aligned videos of people performing the tasks.
1 code implementation • 25 May 2023 • Chenkai Sun, Jinning Li, Hou Pong Chan, ChengXiang Zhai, Heng Ji
Our analysis shows that the best-performing models are capable of predicting responses that are consistent with the personas, and as a byproduct, the task formulation also enables many interesting applications in the analysis of social network groups and their opinions, such as the discovery of extreme opinion groups.
1 code implementation • 24 May 2023 • Qi Zeng, Mankeerat Sidhu, Hou Pong Chan, Lu Wang, Heng Ji
Opinions in the scientific domain can be divergent, leading to controversy or consensus among reviewers.
1 code implementation • 23 May 2023 • Hou Pong Chan, Qi Zeng, Heng Ji
Motivated by how humans inspect factual inconsistency in summaries, we propose an interpretable fine-grained inconsistency detection model, FineGrainFact, which explicitly represents the facts in the documents and summaries with semantic frames extracted by semantic role labeling, and highlights the related semantic frames to predict inconsistency.
no code implementations • 23 May 2023 • Kung-Hsiang Huang, Hou Pong Chan, Kathleen McKeown, Heng Ji
We present a novel task, identifying manipulation of news on social media, which aims to detect manipulation in social media posts and identify manipulated or inserted information.
2 code implementations • 23 May 2023 • Cheng Qian, Chi Han, Yi R. Fung, Yujia Qin, Zhiyuan Liu, Heng Ji
Additionally, we introduce the Creation Challenge dataset, featuring 2K diverse questions, to emphasize the necessity and benefits of LLMs' tool creation ability.
1 code implementation • 23 May 2023 • Qingyun Wang, Doug Downey, Heng Ji, Tom Hope
We explore and enhance the ability of neural language models to generate novel scientific directions grounded in literature.
Contextualized Literature-based Discovery Link Prediction +1
no code implementations • 22 May 2023 • Chi Han, Ziqi Wang, Han Zhao, Heng Ji
Then, we empirically investigate the in-context behaviors of language models.
1 code implementation • 22 May 2023 • Chi Han, Qizheng He, Charles Yu, Xinya Du, Hanghang Tong, Heng Ji
A LERP is designed as a vector of probabilistic logical functions on the entity's neighboring sub-graph.
Ranked #9 on Link Prediction on WN18RR
no code implementations • 22 May 2023 • Chi Han, Jialiang Xu, Manling Li, Yi Fung, Chenkai Sun, Nan Jiang, Tarek Abdelzaher, Heng Ji
As pre-training and fine-tuning are costly and might negatively impact model performance, it is desired to efficiently adapt an existing model to different conditions such as styles, sentiments or narratives, when facing different audiences or scenarios.
no code implementations • 21 May 2023 • Ziqi Wang, Chi Han, Wenxuan Bao, Heng Ji
However, such data augmentation methods are sub-optimal for knowledge distillation since the teacher model could provide label distributions and is more tolerant to semantic shifts.
no code implementations • 19 May 2023 • Revanth Gangi Reddy, Pradeep Dasigi, Md Arafat Sultan, Arman Cohan, Avirup Sil, Heng Ji, Hannaneh Hajishirzi
Neural information retrieval often adopts a retrieve-and-rerank framework: a bi-encoder network first retrieves K (e. g., 100) candidates that are then re-ranked using a more powerful cross-encoder model to rank the better candidates higher.
no code implementations • 19 May 2023 • Tianci Xue, Ziqi Wang, Zhenhailong Wang, Chi Han, Pengfei Yu, Heng Ji
To detect factual inconsistency, RCoT first asks LLMs to reconstruct the problem based on generated solutions.
1 code implementation • NeurIPS 2023 • Zhenhailong Wang, Ansel Blume, Sha Li, Genglin Liu, Jaemin Cho, Zineng Tang, Mohit Bansal, Heng Ji
Action knowledge involves the understanding of textual, visual, and temporal aspects of actions.
Ranked #19 on Video Question Answering on NExT-QA (using extra training data)
1 code implementation • 17 May 2023 • Xingyao Wang, Hao Peng, Reyhaneh Jabbarvand, Heng Ji
We explore LMs' potential to learn from textual interactions (LETI) that not only check their correctness with binary labels but also pinpoint and explain errors in their outputs through textual feedback.
1 code implementation • 13 May 2023 • Kung-Hsiang Huang, Hou Pong Chan, Heng Ji
Faithfully correcting factual errors is critical for maintaining the integrity of textual knowledge bases and preventing hallucinations in sequence-to-sequence models.
3 code implementations • 17 Apr 2023 • Yujia Qin, Shengding Hu, Yankai Lin, Weize Chen, Ning Ding, Ganqu Cui, Zheni Zeng, Yufei Huang, Chaojun Xiao, Chi Han, Yi Ren Fung, Yusheng Su, Huadong Wang, Cheng Qian, Runchu Tian, Kunlun Zhu, Shihao Liang, Xingyu Shen, Bokai Xu, Zhen Zhang, Yining Ye, Bowen Li, Ziwei Tang, Jing Yi, Yuzhang Zhu, Zhenning Dai, Lan Yan, Xin Cong, Yaxi Lu, Weilin Zhao, Yuxiang Huang, Junxi Yan, Xu Han, Xian Sun, Dahai Li, Jason Phang, Cheng Yang, Tongshuang Wu, Heng Ji, Zhiyuan Liu, Maosong Sun
Considering the lack of a systematic tool learning evaluation in prior works, we experiment with 18 representative tools and show the potential of current foundation models in skillfully utilizing tools.
1 code implementation • 25 Mar 2023 • Revanth Gangi Reddy, Yi R. Fung, Qi Zeng, Manling Li, Ziqi Wang, Paul Sullivan, Heng Ji
Further, experiments show that expert analysts tend to add more information into the SmartBook reports, with only 2. 3% of the existing tokens being deleted, meaning SmartBook can serve as a useful foundation for analysts to build upon when creating intelligence reports.
1 code implementation • 16 Mar 2023 • Qiusi Zhan, Sha Li, Kathryn Conger, Martha Palmer, Heng Ji, Jiawei Han
Finally, we perform error analysis and show that label noise is still the largest challenge for improving performance for this new dataset.
no code implementations • 25 Feb 2023 • Tianyi Zhang, Isaac Tham, Zhaoyi Hou, Jiaxuan Ren, Liyang Zhou, Hainiu Xu, Li Zhang, Lara J. Martin, Rotem Dror, Sha Li, Heng Ji, Martha Palmer, Susan Brown, Reece Suchocki, Chris Callison-Burch
Schema induction builds a graph representation explaining how events unfold in a scenario.
no code implementations • 22 Jan 2023 • Tuan Manh Lai, Heng Ji
Leveraging the idea that the coreferential links naturally exist between anchor texts pointing to the same article, our method builds a sizeable distantly-supervised dataset for the target language that consists of tens of thousands of documents.
1 code implementation • 2 Dec 2022 • Revanth Gangi Reddy, Heba Elfardy, Hou Pong Chan, Kevin Small, Heng Ji
A primary objective of news articles is to establish the factual record for an event, frequently achieved by conveying both the details of the specified event (i. e., the 5 Ws; Who, What, Where, When and Why regarding the event) and how people reacted to it (i. e., reported statements).
1 code implementation • 30 Nov 2022 • Sha Li, Heng Ji, Jiawei Han
To tackle this problem, we introduce the idea of type abstraction, where the model is prompted to generalize and name the type.
1 code implementation • 14 Nov 2022 • Xinya Du, Heng Ji
We propose a retrieval-augmented generative QA model (R-GQA) for event argument extraction.
1 code implementation • 10 Nov 2022 • Ke Yang, Charles Yu, Yi Fung, Manling Li, Heng Ji
Despite this, relatively few efforts have been made to debias PLMs by prompt tuning with continuous prompts compared to its discrete counterpart.
1 code implementation • 7 Nov 2022 • Chi Han, Hengzhi Pei, Xinya Du, Heng Ji
To this end, we propose the framework CLORE (Classification by LOgical Reasoning on Explanations).
1 code implementation • 3 Nov 2022 • Yizhu Jiao, Sha Li, Yiqing Xie, Ming Zhong, Heng Ji, Jiawei Han
Specifically, we formulate the role prediction problem as an in-filling task and construct prompts for a pre-trained language model to generate candidate roles.
no code implementations • 3 Nov 2022 • Guang Yang, Manling Li, Jiajie Zhang, Xudong Lin, Shih-Fu Chang, Heng Ji
Video event extraction aims to detect salient events from a video and identify the arguments for each event as well as their semantic roles.
2 code implementations • 31 Oct 2022 • Yangyi Chen, Lifan Yuan, Ganqu Cui, Zhiyuan Liu, Heng Ji
We observe a consistent change in calibration performance across six factors.
1 code implementation • 25 Oct 2022 • Jianhao Shen, Chenguang Wang, Ye Yuan, Jiawei Han, Heng Ji, Koushik Sen, Ming Zhang, Dawn Song
For instance, we outperform the fully finetuning approaches on a KG completion benchmark by tuning only 1% of the parameters.
Ranked #5 on Link Prediction on UMLS
1 code implementation • 23 Oct 2022 • Xingyao Wang, Sha Li, Heng Ji
As a case study, we formulate Event Argument Extraction (EAE) as converting text into event-argument structures that can be represented as a class object using code.
1 code implementation • 23 Oct 2022 • Liliang Ren, Zixuan Zhang, Han Wang, Clare R. Voss, ChengXiang Zhai, Heng Ji
Modern large-scale Pre-trained Language Models (PLMs) have achieved tremendous success on a wide range of downstream tasks.
Ranked #6 on Few-shot NER on Few-NERD (INTRA) (using extra training data)
1 code implementation • 22 Oct 2022 • Long Chen, Yulei Niu, Brian Chen, Xudong Lin, Guangxing Han, Christopher Thomas, Hammad Ayyubi, Heng Ji, Shih-Fu Chang
Specifically, given an article and a relevant video, WSAG aims to localize all ``groundable'' sentences to the video, and these sentences are possibly at different semantic scales.
1 code implementation • 21 Oct 2022 • Ziqi Wang, Yuexin Wu, Frederick Liu, Daogao Liu, Le Hou, Hongkun Yu, Jing Li, Heng Ji
However, these data augmentation methods either potentially cause shifts in decision boundaries (representation interpolation), are not expressive enough (token replacement), or introduce too much computational overhead (augmentation with models).
1 code implementation • 16 Oct 2022 • Yi R. Fung, Tuhin Chakraborty, Hao Guo, Owen Rambow, Smaranda Muresan, Heng Ji
Norm discovery is important for understanding and reasoning about the acceptable behaviors and potential violations in human communication and interactions.
2 code implementations • 13 Oct 2022 • Ming Zhong, Yang Liu, Da Yin, Yuning Mao, Yizhu Jiao, PengFei Liu, Chenguang Zhu, Heng Ji, Jiawei Han
We re-frame NLG evaluation as a Boolean Question Answering (QA) task, and by guiding the model with different questions, we can use one evaluator to evaluate from multiple dimensions.
no code implementations • 9 Oct 2022 • Feng Wang, Manling Li, Xudong Lin, Hairong Lv, Alexander G. Schwing, Heng Ji
Recent advances in pre-training vision-language models like CLIP have shown great potential in learning transferable visual representations.
1 code implementation • 1 Oct 2022 • Zhenhailong Wang, Xiaoman Pan, Dian Yu, Dong Yu, Jianshu Chen, Heng Ji
Notably, our proposed $\text{Zemi}_\text{LARGE}$ outperforms T0-3B by 16% on all seven evaluation tasks while being 3. 9x smaller in model size.
1 code implementation • ACL 2022 • Xinya Du, Sha Li, Heng Ji
Extracting informative arguments of events from news articles is a challenging problem in information extraction, which requires a global contextual understanding of each document.
1 code implementation • COLING 2022 • Kung-Hsiang Huang, ChengXiang Zhai, Heng Ji
Given the absence of cross-lingual information retrieval datasets with claim-like queries, we train the retriever with our proposed Cross-lingual Inverse Cloze Task (X-ICT), a self-supervised algorithm that creates training instances by translating the title of a passage.
Ranked #1 on Zero-shot Cross-lingual Fact-checking on X-Fact
Cross-lingual Fact-checking Cross-Lingual Information Retrieval +4
1 code implementation • 31 Aug 2022 • Chenkai Sun, Tie XU, ChengXiang Zhai, Heng Ji
In this paper, we present Tetris, a new task of Goal-Oriented Script Completion.
1 code implementation • 25 Aug 2022 • Qingyun Wang, Manling Li, Hou Pong Chan, Lifu Huang, Julia Hockenmaier, Girish Chowdhary, Heng Ji
Goal-oriented generative script learning aims to generate subsequent steps to reach a particular goal, which is an essential task to assist robots or humans in performing stereotypical activities.
no code implementations • 15 Jun 2022 • Sha Li, Mahdi Namazifar, Di Jin, Mohit Bansal, Heng Ji, Yang Liu, Dilek Hakkani-Tur
Providing conversation models with background knowledge has been shown to make open-domain dialogues more informative and engaging.
no code implementations • 6 Jun 2022 • Hongwei Wang, Zixuan Zhang, Sha Li, Jiawei Han, Yizhou Sun, Hanghang Tong, Joseph P. Olive, Heng Ji
Existing link prediction or graph completion methods have difficulty dealing with event graphs because they are usually designed for a single large graph such as a social network or a knowledge graph, rather than multiple small dynamic event graphs.
1 code implementation • CVPR 2023 • Xudong Lin, Simran Tiwari, Shiyuan Huang, Manling Li, Mike Zheng Shou, Heng Ji, Shih-Fu Chang
We surprisingly find that discrete text tokens coupled with a pretrained contrastive text model yields the best performance, which can even outperform state-of-the-art on the iVQA and How2QA datasets without additional training on millions of video-text data.
Ranked #1 on Video Question Answering on iVQA
1 code implementation • 30 May 2022 • Qi Zeng, Qiusi Zhan, Heng Ji
Events are inter-related in documents.
no code implementations • 23 May 2022 • Anish Saha, Amith Ananthram, Emily Allaway, Heng Ji, Kathleen McKeown
Practitioners from many disciplines (e. g., political science) use expert-crafted taxonomies to make sense of large, unlabeled corpora.
no code implementations • 23 May 2022 • Yubin Ge, Ziang Xiao, Jana Diesner, Heng Ji, Karrie Karahalios, Hari Sundaram
We constructed a new human-annotated dataset of human-written follow-up questions with dialogue history and labeled knowledge in the context of conversational surveys.
1 code implementation • 22 May 2022 • Zhenhailong Wang, Manling Li, Ruochen Xu, Luowei Zhou, Jie Lei, Xudong Lin, Shuohang Wang, ZiYi Yang, Chenguang Zhu, Derek Hoiem, Shih-Fu Chang, Mohit Bansal, Heng Ji
The goal of this work is to build flexible video-language models that can generalize to various video-to-text tasks from few examples, such as domain-specific captioning, question answering, and future event prediction.
1 code implementation • 25 Apr 2022 • Carl Edwards, Tuan Lai, Kevin Ros, Garrett Honke, Kyunghyun Cho, Heng Ji
We present $\textbf{MolT5}$ $-$ a self-supervised learning framework for pretraining models on a vast amount of unlabeled natural language text and molecule strings.
Ranked #4 on Text-based de novo Molecule Generation on ChEBI-20
1 code implementation • 24 Apr 2022 • Revanth Gangi Reddy, Md Arafat Sultan, Martin Franz, Avirup Sil, Heng Ji
On two public IR benchmarks, we empirically show that the proposed method helps improve both the model's attention patterns and retrieval performance, including in zero-shot settings.
1 code implementation • 10 Mar 2022 • Kung-Hsiang Huang, Kathleen McKeown, Preslav Nakov, Yejin Choi, Heng Ji
Despite recent advances in detecting fake news generated by neural models, their results are not readily applicable to effective detection of human-written disinformation.
1 code implementation • 9 Mar 2022 • Yi R. Fung, Heng Ji
Online social networks such as Twitter and Weibo play an important role in how people stay informed and exchange reactions.
1 code implementation • MMMPIE (COLING) 2022 • Zhenhailong Wang, Hang Yu, Manling Li, Han Zhao, Heng Ji
While much literature has been devoted to exploring alternative optimization strategies, we identify another essential aspect towards effective few-shot transfer learning, task sampling, which is previously only be viewed as part of data pre-processing in MAML.
1 code implementation • Findings (ACL) 2022 • Tuan Manh Lai, Heng Ji, ChengXiang Zhai
We use the profile to query the indexed search engine to retrieve candidate entities.
no code implementations • 15 Feb 2022 • Sha Li, Liyuan Liu, Yiqing Xie, Heng Ji, Jiawei Han
Our framework decomposes event detection into an identification task and a localization task.
no code implementations • 12 Feb 2022 • Carl Edwards, Heng Ji
In contrast, we present a novel approach to semi-supervised new event type induction using a masked contrastive loss, which learns similarities between event mentions by enforcing an attention mechanism over the data minibatch.
1 code implementation • CVPR 2022 • Manling Li, Ruochen Xu, Shuohang Wang, Luowei Zhou, Xudong Lin, Chenguang Zhu, Michael Zeng, Heng Ji, Shih-Fu Chang
Vision-language (V+L) pretraining models have achieved great success in supporting multimedia applications by understanding the alignments between images and text.
2 code implementations • 20 Dec 2021 • Revanth Gangi Reddy, Xilin Rui, Manling Li, Xudong Lin, Haoyang Wen, Jaemin Cho, Lifu Huang, Mohit Bansal, Avirup Sil, Shih-Fu Chang, Alexander Schwing, Heng Ji
Specifically, the task involves multi-hop questions that require reasoning over image-caption pairs to identify the grounded visual object being referred to and then predicting a span from the news body text to answer the question.
2 code implementations • 16 Dec 2021 • Revanth Gangi Reddy, Sai Chetan, Zhenhailong Wang, Yi R. Fung, Kathryn Conger, Ahmed Elsayed, Martha Palmer, Preslav Nakov, Eduard Hovy, Kevin Small, Heng Ji
In this work, we present NewsClaims, a new benchmark for attribute-aware claim detection in the news domain.
1 code implementation • NAACL 2022 • Yifan Chen, Qi Zeng, Dilek Hakkani-Tur, Di Jin, Heng Ji, Yun Yang
Transformer-based models are not efficient in processing long sequences due to the quadratic space and time complexity of the self-attention modules.
1 code implementation • 5 Dec 2021 • Zhenhailong Wang, Heng Ji
State-of-the-art brain-to-text systems have achieved great success in decoding language directly from brain signals using neural networks.
1 code implementation • NeurIPS 2021 • Yifan Chen, Qi Zeng, Heng Ji, Yun Yang
Transformers are expensive to train due to the quadratic time and space complexity in the self-attention mechanism.
no code implementations • ICLR 2022 • Ruicheng Xian, Heng Ji, Han Zhao
Recent advances in neural modeling have produced deep multilingual language models capable of extracting cross-lingual knowledge from unparallel texts, as evidenced by their decent zero-shot transfer performance.
no code implementations • Findings (EMNLP) 2021 • Brian Chen, Xudong Lin, Christopher Thomas, Manling Li, Shoya Yoshida, Lovish Chum, Heng Ji, Shih-Fu Chang
We introduce the new task of Video MultiMedia Event Extraction (Video M2E2) and propose two novel components to build the first system towards this task.
1 code implementation • ICLR 2022 • Hongwei Wang, Weijiang Li, Xiaomeng Jin, Kyunghyun Cho, Heng Ji, Jiawei Han, Martin D. Burke
Molecule representation learning (MRL) methods aim to embed molecules into a real vector space.
1 code implementation • EMNLP 2021 • Yu Meng, Yunyi Zhang, Jiaxin Huang, Xuan Wang, Yu Zhang, Heng Ji, Jiawei Han
We study the problem of training named entity recognition (NER) models using only distantly-labeled data, which can be automatically obtained by matching entity mentions in the raw text with entity types in a knowledge base.
1 code implementation • EMNLP 2021 • Jiaming Shen, Yunyi Zhang, Heng Ji, Jiawei Han
As events of the same type could be expressed in multiple ways, we propose to represent each event type as a cluster of <predicate sense, object head> pairs.
1 code implementation • Findings (EMNLP) 2021 • Tuan Lai, Heng Ji, ChengXiang Zhai
Biomedical entity linking is the task of linking entity mentions in a biomedical document to referent entities in a knowledge base.
no code implementations • 3 Sep 2021 • Daniel Campos, Heng Ji
A large portion of chemistry literature focuses on new molecules and reactions between molecules.
no code implementations • 29 Aug 2021 • Chenkai Sun, Weijiang Li, Jinfeng Xiao, Nikolaus Nova Parulian, ChengXiang Zhai, Heng Ji
Automated knowledge discovery from trending chemical literature is essential for more efficient biomedical research.
no code implementations • 23 Aug 2021 • Tuan Manh Lai, Yang Zhang, Evelina Bakhturina, Boris Ginsburg, Heng Ji
In addition, we also create a cleaned dataset from the Spoken Wikipedia Corpora for German and report the performance of our systems on the dataset.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
no code implementations • ACL 2021 • Muhao Chen, Hongming Zhang, Qiang Ning, Manling Li, Heng Ji, Kathleen McKeown, Dan Roth
This tutorial targets researchers and practitioners who are interested in AI technologies that help machines understand natural language text, particularly real-world events described in the text.
no code implementations • ACL 2021 • Samuel Kriman, Heng Ji
The tasks performed by this system are entity and event identification, typing, and coreference resolution.
no code implementations • ACL 2021 • Yi Fung, Christopher Thomas, Revanth Gangi Reddy, Sandeep Polisetty, Heng Ji, Shih-Fu Chang, Kathleen McKeown, Mohit Bansal, Avi Sil
To defend against machine-generated fake news, an effective mechanism is urgently needed.
no code implementations • ACL 2021 • Zixuan Zhang, Nikolaus Parulian, Heng Ji, Ahmed Elsayed, Skatje Myers, Martha Palmer
In this paper, we propose a novel biomedical Information Extraction (IE) model to tackle these two challenges and extract scientific entities and events from English research papers.
1 code implementation • CVPR 2021 • Spencer Whitehead, Hui Wu, Heng Ji, Rogerio Feris, Kate Saenko
Generalization to out-of-distribution data has been a problem for Visual Question Answering (VQA) models.
1 code implementation • Findings (ACL) 2021 • Liliang Ren, Chenkai Sun, Heng Ji, Julia Hockenmaier
Text-to-Graph extraction aims to automatically extract information graphs consisting of mentions and types from natural language texts.
Ranked #1 on Relation Extraction on ACE 2005 (Sentence Encoder metric)
1 code implementation • NAACL 2021 • Zixuan Zhang, Heng Ji
The tasks of Rich Semantic Parsing, such as Abstract Meaning Representation (AMR), share similar goals with Information Extraction (IE) to convert natural language texts into structured semantic representations.
1 code implementation • NAACL 2021 • Haoyang Wen, Yanru Qu, Heng Ji, Qiang Ning, Jiawei Han, Avi Sil, Hanghang Tong, Dan Roth
Grounding events into a precise timeline is important for natural language understanding but has received limited attention in recent work.
no code implementations • NAACL 2021 • Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li
Due to its great power in modeling non-Euclidean data like graphs or manifolds, deep learning on graph techniques (i. e., Graph Neural Networks (GNNs)) have opened a new door to solving challenging graph-related NLP problems.
1 code implementation • NAACL 2021 • Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, D{\'\i}dac Sur{\'\i}s, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji
We present a new information extraction system that can automatically construct temporal event graphs from a collection of news documents from multiple sources, multiple languages (English and Spanish for our experiment), and multiple data modalities (speech, text, image and video).
1 code implementation • ACL 2021 • Tuan Lai, Heng Ji, ChengXiang Zhai, Quan Hung Tran
It then uses an entity linker to form a knowledge graph containing relevant background knowledge for the the entity mentions in the text.
1 code implementation • NeurIPS 2021 • Yifan Chen, Qi Zeng, Heng Ji, Yun Yang
Transformers are expensive to train due to the quadratic time and space complexity in the self-attention mechanism.
1 code implementation • ACL 2021 • Qingyun Wang, Semih Yavuz, Victoria Lin, Heng Ji, Nazneen Rajani
Graph-to-text generation has benefited from pre-trained language models (PLMs) in achieving better performance than structured graph encoders.
Ranked #3 on Data-to-Text Generation on WebNLG (using extra training data)
2 code implementations • Findings (ACL) 2021 • Chi Han, Mingxuan Wang, Heng Ji, Lei LI
By projecting audio and text features to a common semantic representation, Chimera unifies MT and ST tasks and boosts the performance on ST benchmarks, MuST-C and Augmented Librispeech, to a new state-of-the-art.
no code implementations • ACL 2021 • Haoyang Wen, Anthony Ferritto, Heng Ji, Radu Florian, Avirup Sil
Existing models on Machine Reading Comprehension (MRC) require complex model architecture for effectively modeling long texts with paragraph representation and classification, thereby making inference computationally inefficient for production use.
no code implementations • 15 Apr 2021 • Revanth Gangi Reddy, Vikas Yadav, Md Arafat Sultan, Martin Franz, Vittorio Castelli, Heng Ji, Avirup Sil
Recent work has shown that commonly available machine reading comprehension (MRC) datasets can be used to train high-performance neural information retrieval (IR) systems.
1 code implementation • NAACL 2021 • Sha Li, Heng Ji, Jiawei Han
On the task of argument extraction, we achieve an absolute gain of 7. 6% F1 and 5. 7% F1 over the next best model on the RAMS and WikiEvents datasets respectively.
Document-level Event Extraction Event Argument Extraction +2
1 code implementation • EMNLP 2021 • Manling Li, Sha Li, Zhenhailong Wang, Lifu Huang, Kyunghyun Cho, Heng Ji, Jiawei Han, Clare Voss
We introduce a new concept of Temporal Complex Event Schema: a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations.
no code implementations • 6 Apr 2021 • Ying Lin, Han Wang, Jiangning Chen, Tong Wang, Yue Liu, Heng Ji, Yang Liu, Premkumar Natarajan
For example, with "add milk to my cart", a customer may refer to a certain organic product, while some customers may want to re-order products they regularly purchase.
1 code implementation • NAACL 2021 • Luyang Huang, Shuyang Cao, Nikolaus Parulian, Heng Ji, Lu Wang
The quadratic computational and memory complexities of large Transformers have limited their scalability for long document summarization.
1 code implementation • NAACL 2021 • Tuan Lai, Heng Ji, Trung Bui, Quan Hung Tran, Franck Dernoncourt, Walter Chang
Event coreference resolution is an important research problem with many applications.
no code implementations • 23 Feb 2021 • Huajie Shao, Jun Wang, Haohong Lin, Xuezhou Zhang, Aston Zhang, Heng Ji, Tarek Abdelzaher
The algorithm is injected into a Conditional Variational Autoencoder (CVAE), allowing \textit{Apex} to control both (i) the order of keywords in the generated sentences (conditioned on the input keywords and their order), and (ii) the trade-off between diversity and accuracy.
no code implementations • 25 Jan 2021 • Thamar Solorio, Mahsa Shafaei, Christos Smailis, Mona Diab, Theodore Giannakopoulos, Heng Ji, Yang Liu, Rada Mihalcea, Smaranda Muresan, Ioannis Kakadiaris
This white paper presents a summary of the discussions regarding critical considerations to develop an extensive repository of online videos annotated with labels indicating questionable content.
1 code implementation • ACL 2021 • Yujia Qin, Yankai Lin, Ryuichi Takanobu, Zhiyuan Liu, Peng Li, Heng Ji, Minlie Huang, Maosong Sun, Jie zhou
Pre-trained Language Models (PLMs) have shown superior performance on various downstream Natural Language Processing (NLP) tasks.
1 code implementation • 26 Nov 2020 • Spencer Whitehead, Hui Wu, Yi Ren Fung, Heng Ji, Rogerio Feris, Kate Saenko
Existing Visual Question Answering (VQA) models are often fragile and sensitive to input variations.
1 code implementation • 9 Nov 2020 • Xiaodan Hu, Pengfei Yu, Kevin Knight, Heng Ji, Bo Li, Honghui Shi
Experiments show that our approach can accurately illustrate 78% textual attributes, which also help MUSE capture the subject in a more creative and expressive way.
no code implementations • 6 Nov 2020 • Lihui Liu, Boxin Du, Heng Ji, Hanghang Tong
In detail, we develop KompaRe, the first of its kind prototype system that provides comparative reasoning capability over large knowledge graphs.
2 code implementations • 24 Oct 2020 • Yuning Mao, Xiang Ren, Heng Ji, Jiawei Han
Despite significant progress, state-of-the-art abstractive summarization methods are still prone to hallucinate content inconsistent with the source document.
no code implementations • CONLL 2018 • Boliang Zhang, Spencer Whitehead, Lifu Huang, Heng Ji
Many name tagging approaches use local contextual information with much success, but fail when the local context is ambiguous or limited.
2 code implementations • EMNLP 2020 • Yu Meng, Yunyi Zhang, Jiaxin Huang, Chenyan Xiong, Heng Ji, Chao Zhang, Jiawei Han
In this paper, we explore the potential of only using the label name of each class to train classification models on unlabeled data, without using any labeled documents.
1 code implementation • INLG (ACL) 2020 • Qingyun Wang, Qi Zeng, Lifu Huang, Kevin Knight, Heng Ji, Nazneen Fatema Rajani
To assist human review process, we build a novel ReviewRobot to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison.
1 code implementation • EMNLP 2020 • Jiaxin Huang, Yu Meng, Fang Guo, Heng Ji, Jiawei Han
Aspect-based sentiment analysis of review texts is of great value for understanding user feedback in a fine-grained manner.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2
3 code implementations • 9 Oct 2020 • Wenhao Yu, Chenguang Zhu, Zaitang Li, Zhiting Hu, Qingyun Wang, Heng Ji, Meng Jiang
To address this issue, researchers have considered incorporating various forms of knowledge beyond the input text into the generation models.
1 code implementation • EMNLP 2020 • Jiaming Shen, Heng Ji, Jiawei Han
Linguistic steganography studies how to hide secret messages in natural language cover texts.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Ranran Haoran Zhang, Qianying Liu, Aysa Xuemo Fan, Heng Ji, Daojian Zeng, Fei Cheng, Daisuke Kawahara, Sadao Kurohashi
We propose a novel Sequence-to-Unordered-Multi-Tree (Seq2UMTree) model to minimize the effects of exposure bias by limiting the decoding length to three within a triplet and removing the order among triplets.
no code implementations • ACL 2020 • Ying Lin, Heng Ji, Fei Huang, Lingfei Wu
OneIE performs end-to-end IE in four stages: (1) Encoding a given sentence as contextualized word representations; (2) Identifying entity mentions and event triggers as nodes; (3) Computing label scores for all nodes and their pairwise links using local classifiers; (4) Searching for the globally optimal graph with a beam decoder.
no code implementations • NAACL 2021 • Qingyun Wang, Manling Li, Xuan Wang, Nikolaus Parulian, Guangxing Han, Jiawei Ma, Jingxuan Tu, Ying Lin, Haoran Zhang, Weili Liu, Aabhas Chauhan, Yingjun Guan, Bangzheng Li, Ruisong Li, Xiangchen Song, Yi R. Fung, Heng Ji, Jiawei Han, Shih-Fu Chang, James Pustejovsky, Jasmine Rah, David Liem, Ahmed Elsayed, Martha Palmer, Clare Voss, Cynthia Schneider, Boyan Onyshkevych
To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and related biological functions.
no code implementations • ACL 2020 • Manling Li, Alireza Zareian, Ying Lin, Xiaoman Pan, Spencer Whitehead, Brian Chen, Bo Wu, Heng Ji, Shih-Fu Chang, Clare Voss, Daniel Napierski, Marjorie Freedman
We present the first comprehensive, open source multimedia knowledge extraction system that takes a massive stream of unstructured, heterogeneous multimedia data from various sources and languages as input, and creates a coherent, structured knowledge base, indexing entities, relations, and events, following a rich, fine-grained ontology.
no code implementations • ACL 2020 • Manling Li, Alireza Zareian, Qi Zeng, Spencer Whitehead, Di Lu, Heng Ji, Shih-Fu Chang
We introduce a new task, MultiMedia Event Extraction (M2E2), which aims to extract events and their arguments from multimedia documents.
no code implementations • LREC 2020 • Di Lu, Ananya Subburathinam, Heng Ji, Jonathan May, Shih-Fu Chang, Avi Sil, Clare Voss
Most of the current cross-lingual transfer learning methods for Information Extraction (IE) have been only applied to name tagging.
no code implementations • 11 Feb 2020 • Tongtao Zhang, Heng Ji, Shih-Fu Chang, Marjorie Freedman
In this paper, we address a practical scenario where training data is released in a sequence of small-scale batches and annotation in earlier phases has lower quality than the later counterparts.
1 code implementation • ECCV 2020 • Dídac Surís, Dave Epstein, Heng Ji, Shih-Fu Chang, Carl Vondrick
Language acquisition is the process of learning words from the surrounding scene.
no code implementations • IJCNLP 2019 • Ananya Subburathinam, Di Lu, Heng Ji, Jonathan May, Shih-Fu Chang, Avirup Sil, Clare Voss
The identification of complex semantic structures such as events and entity relations, already a challenging Information Extraction task, is doubly difficult from sources written in under-resourced and under-annotated languages.
no code implementations • WS 2019 • Kevin Blissett, Heng Ji
In this paper we address a challenging cross-lingual name retrieval task.
no code implementations • WS 2019 • Xiaoman Pan, Thamme Gowda, Heng Ji, Jonathan May, Scott Miller
Because this multilingual common space directly relates the semantics of contextual words in the source language to that of entities in the target language, we leverage it for unsupervised cross-lingual entity linking.
no code implementations • IJCNLP 2019 • Ying Lin, Heng Ji
In addition, we propose a two-step mention-aware attention mechanism to enable the model to focus on important words in mentions and contexts.
no code implementations • WS 2019 • Diya Li, Heng Ji
In this paper we tackle two unique challenges in biomedical relation extraction.
1 code implementation • IJCNLP 2019 • Yixin Cao, Zikun Hu, Tat-Seng Chua, Zhiyuan Liu, Heng Ji
Name tagging in low-resource languages or domains suffers from inadequate training data.
1 code implementation • 14 Aug 2019 • Liyuan Liu, Zihan Wang, Jingbo Shang, Dandong Yin, Heng Ji, Xiang Ren, Shaowen Wang, Jiawei Han
Our model neither requires the conversion from character sequences to word sequences, nor assumes tokenizer can correctly detect all word boundaries.
no code implementations • ACL 2019 • Manling Li, Lingyu Zhang, Heng Ji, Richard J. Radke
Transcripts of natural, multi-person meetings differ significantly from documents like news articles, which can make Natural Language Generation models for generating summaries unfocused.
1 code implementation • ACL 2019 • Ying Lin, Liyuan Liu, Heng Ji, Dong Yu, Jiawei Han
We design a set of word frequency-based reliability signals to indicate the quality of each word embedding.
no code implementations • NAACL 2019 • Diya Li, Lifu Huang, Heng Ji, Jiawei Han
Event extraction for the biomedical domain is more challenging than that in the general news domain since it requires broader acquisition of domain-specific knowledge and deeper understanding of complex contexts.
no code implementations • NAACL 2019 • Manling Li, Ying Lin, Joseph Hoover, Spencer Whitehead, Clare Voss, Morteza Dehghani, Heng Ji
This paper demonstrates a state-of-the-art end-to-end multilingual (English, Russian, and Ukrainian) knowledge extraction system that can perform entity discovery and linking, relation extraction, event extraction, and coreference.