no code implementations • 24 Dec 2022 • Borui Wang, Chengcheng Feng, Arjun Nair, Madelyn Mao, Jai Desai, Asli Celikyilmaz, Haoran Li, Yashar Mehdad, Dragomir Radev
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 abstractive dialogue summarization can also be used as a means to boost an NLP system's performance on other important dialogue comprehension tasks.
no code implementations • NAACL 2022 • Xiangru Tang, Arjun Nair, Borui Wang, Bingyao Wang, Jai Desai, Aaron Wade, Haoran Li, Asli Celikyilmaz, Yashar Mehdad, Dragomir Radev
Using human evaluation and automatic faithfulness metrics, we show that our model significantly reduces all kinds of factual errors on the dialogue summarization, SAMSum corpus.
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.
no code implementations • NAACL 2022 • Xiangru Tang, Alexander Fabbri, Haoran Li, Ziming Mao, Griffin Thomas Adams, Borui Wang, Asli Celikyilmaz, Yashar Mehdad, Dragomir Radev
Current pre-trained models applied to summarization are prone to factual inconsistencies which either misrepresent the source text or introduce extraneous information.
1 code implementation • ACL 2021 • Alexander R. Fabbri, Faiaz Rahman, Imad Rizvi, Borui Wang, Haoran Li, Yashar Mehdad, Dragomir Radev
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.
no code implementations • ICCV 2019 • Borui Wang, Ehsan Adeli, Hsu-kuang Chiu, De-An Huang, Juan Carlos Niebles
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 various architectures of recurrent neural networks.
Ranked #2 on
Human Pose Forecasting
on Human3.6M
(MAR, walking, 1,000ms metric)
1 code implementation • 23 Oct 2018 • Hsu-kuang Chiu, Ehsan Adeli, Borui Wang, De-An Huang, Juan Carlos Niebles
In this paper, we propose a new action-agnostic method for short- and long-term human pose forecasting.
Ranked #5 on
Human Pose Forecasting
on Human3.6M
(MAR, walking, 1,000ms metric)
no code implementations • 6 Dec 2017 • Borui Wang, Geoffrey Gordon
Learning general latent-variable probabilistic graphical models is a key theoretical challenge in machine learning and artificial intelligence.