Li Deng


2018

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Tensor Product Generation Networks for Deep NLP Modeling
Qiuyuan Huang | Paul Smolensky | Xiaodong He | Li Deng | Dapeng Wu
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

We present a new approach to the design of deep networks for natural language processing (NLP), based on the general technique of Tensor Product Representations (TPRs) for encoding and processing symbol structures in distributed neural networks. A network architecture — the Tensor Product Generation Network (TPGN) — is proposed which is capable in principle of carrying out TPR computation, but which uses unconstrained deep learning to design its internal representations. Instantiated in a model for image-caption generation, TPGN outperforms LSTM baselines when evaluated on the COCO dataset. The TPR-capable structure enables interpretation of internal representations and operations, which prove to contain considerable grammatical content. Our caption-generation model can be interpreted as generating sequences of grammatical categories and retrieving words by their categories from a plan encoded as a distributed representation.

2017

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

This paper proposes KB-InfoBot - a multi-turn dialogue agent which helps users search Knowledge Bases (KBs) without composing complicated queries. Such goal-oriented dialogue agents typically need to interact with an external database to access real-world knowledge. Previous systems achieved this by issuing a symbolic query to the KB to retrieve entries based on their attributes. However, such symbolic operations break the differentiability of the system and prevent end-to-end training of neural dialogue agents. In this paper, we address this limitation by replacing symbolic queries with an induced “soft” posterior distribution over the KB that indicates which entities the user is interested in. Integrating the soft retrieval process with a reinforcement learner leads to higher task success rate and reward in both simulations and against real users. We also present a fully neural end-to-end agent, trained entirely from user feedback, and discuss its application towards personalized dialogue agents.

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Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension
David Golub | Po-Sen Huang | Xiaodong He | Li Deng
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We develop a technique for transfer learning in machine comprehension (MC) using a novel two-stage synthesis network. Given a high performing MC model in one domain, our technique aims to answer questions about documents in another domain, where we use no labeled data of question-answer pairs. Using the proposed synthesis network with a pretrained model on the SQuAD dataset, we achieve an F1 measure of 46.6% on the challenging NewsQA dataset, approaching performance of in-domain models (F1 measure of 50.0%) and outperforming the out-of-domain baseline by 7.6%, without use of provided annotations.

2016

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Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads
Ji He | Mari Ostendorf | Xiaodong He | Jianshu Chen | Jianfeng Gao | Lihong Li | Li Deng
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Bi-directional Attention with Agreement for Dependency Parsing
Hao Cheng | Hao Fang | Xiaodong He | Jianfeng Gao | Li Deng
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Deep Reinforcement Learning with a Natural Language Action Space
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)

2015

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

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Language Models for Image Captioning: The Quirks and What Works
Jacob Devlin | Hao Cheng | Hao Fang | Saurabh Gupta | Li Deng | Xiaodong He | Geoffrey Zweig | Margaret Mitchell
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

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Learning Continuous Phrase Representations for Translation Modeling
Jianfeng Gao | Xiaodong He | Wen-tau Yih | Li Deng
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Modeling Interestingness with Deep Neural Networks
Jianfeng Gao | Patrick Pantel | Michael Gamon | Xiaodong He | Li Deng
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2012

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Maximum Expected BLEU Training of Phrase and Lexicon Translation Models
Xiaodong He | Li Deng
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)