42 citations · 48 across the 8 of their papers we have counts for
9 papers
DYSTIL: Dynamic Strategy Induction with Large Language Models for Reinforcement Learning
Borui Wang, Kathleen McKeown, Rex Ying
Reinforcement learning from expert demonstrations has long remained a challenging research problem, and existing state-of-the-art methods using behavioral cloning plus further RL t…
STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension
Borui Wang, Chengcheng Feng, Arjun Nair +6
Abstractive dialogue summarization has long been viewed as an important standalone task in natural language processing, but no previous work has explored the possibility of whether…
CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning
Xiangru Tang, Arjun Nair, Borui Wang +7
Factual inconsistencies in generated summaries severely limit the practical applications of abstractive dialogue summarization. Although significant progress has been achieved by u…
KAT: A Knowledge Augmented Transformer for Vision-and-Language
Liangke Gui, Borui Wang, Qiuyuan Huang +3
The primary focus of recent work with largescale transformers has been on optimizing the amount of information packed into the model's parameters. In this work, we ask a different…
Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries
Xiangru Tang, Alexander Fabbri, Haoran Li +6
Current pre-trained models applied to summarization are prone to factual inconsistencies which either misrepresent the source text or introduce extraneous information. Thus, compar…
ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining
Alexander R. Fabbri, Faiaz Rahman, Imad Rizvi +4
While online conversations can cover a vast amount of information in many different formats, abstractive text summarization has primarily focused on modeling solely news articles.…
Imitation Learning for Human Pose Prediction
Borui Wang, Ehsan Adeli, Hsu-kuang Chiu +2
Modeling and prediction of human motion dynamics has long been a challenging problem in computer vision, and most existing methods rely on the end-to-end supervised training of var…
Action-Agnostic Human Pose Forecasting
Hsu-kuang Chiu, Ehsan Adeli, Borui Wang +2
Predicting and forecasting human dynamics is a very interesting but challenging task with several prospective applications in robotics, health-care, etc. Recently, several methods…
Learning General Latent-Variable Graphical Models with Predictive Belief Propagation
Borui Wang, Geoffrey Gordon
Learning general latent-variable probabilistic graphical models is a key theoretical challenge in machine learning and artificial intelligence. All previous methods, including the…