Li Deng

End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager

2016 · arXiv (Cornell University)

Natural language understanding and dialogue policy learning are both essential in conversational systems that predict the next system actions in response to a current user utterance. Conventional approaches aggregate separate models of natural language understanding …

Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access

2017

Bhuwan Dhingra, Lihong Li, Xiujun Li, Jianfeng Gao, Yun-Nung Chen, Faisal Ahmed, Li Deng. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2017.

Deep Reinforcement Learning with a Natural Language Action Space

2016

Ji He, Jianshu Chen, Xiaodong He, Jianfeng Gao, Lihong Li, Li Deng, Mari Ostendorf. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.

EALD-MLLM: Emotion Analysis in Long-sequential and De-identity videos with Multi-modal Large Language Model

2024 · arXiv (Cornell University)

Emotion AI is the ability of computers to understand human emotional states. Existing works have achieved promising progress, but two limitations remain to be solved: 1) Previous studies have been more focused on short sequential …

Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval

2015

Xiaodong Liu, Jianfeng Gao, Xiaodong He, Li Deng, Kevin Duh, Ye-yi Wang. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2015.

End-to-End Memory Networks with Knowledge Carryover for Multi-Turn Spoken Language Understanding

2016

Spoken language understanding (SLU) is a core component of a spoken dialogue system. In the traditional architecture of dialogue systems, the SLU component treats each utterance independent of each other, and then the following components …

MS MARCO: A Human Generated MAchine Reading COmprehension Dataset.

2016 · Neural Information Processing Systems

This paper presents our recent work on the design and development of a new, large scale dataset, which we name MS MARCO, for MAchine Reading COmprehension. This new dataset is aimed to overcome a number …