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Applications to Natural Language Processing (NLP)

How can we introduce decision making abilities to natural language-based systems? Some of the these NLP applications such as conversational systems can naturally be modeled in a decision-theoretic framework and optimized by reinforcement learning.
  • J. Gao, M. Galley, and L. Li: Neural approaches to Conversational AI: Question answering, task-oriented dialogues and social chatbots. Foundations and Trends in Information Retrieval, 13(2-3):127-298, 2019. ISBN 978-1-68083-552-6. [link, arXiv]
  • D. Tang, X. Li, J. Gao, C. Wang, L. Li, and T. Jebara: Subgoal discovery for hierarchical dialogue policy learning. In the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018. [link]
  • Z. Lipton, X. Li, J. Gao, L. Li, F. Ahmed, and L. Deng: Efficient dialogue policy learning with BBQ-networks. In the 32nd AAAI Conference on Artificial Intelligence (AAAI), 2018. [link]
  • J. Chen, C. Wang, L. Xiao, J. He, L. Li, and L. Deng: Q-LDA: Uncovering latent patterns in text-based sequential decision processes. In Advances in Neural Information Processing Systems 30 (NIPS), 2017. [link]
  • B. Peng, X. Li, L. Li, J. Gao, A. Celikyilmaz, S. Lee, and K.-F. Wong: Composite task-completion dialogue system via hierarchical deep reinforcement learning. In the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2017. [link]
  • B. Dhingra, L. Li, X. Li, J. Gao, Y.-N. Chen, F. Ahmed, and L. Deng: Towards end-to-end reinforcement learning of dialogue agents for information access. In the 55th Annual Meeting of the Association for Computational Linguistics (ACL), 2017. [link]
  • X. Li, Z.C. Lipton, B. Dhingra, L. Li, J. Gao, Y.-N. Chen: A user simulator for task-completion dialogues. MSR technical report, December 2016.
  • J. He, M. Ostendorf, X. He, J. Chen, J. Gao, L. Li, and L. Deng: Deep reinforcement learning with a combinatorial action space for predicting and tracking popular discussion threads. In the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2016. [link]
  • J. He, J. Chen, X. He, J. Gao, L. Li, L. Deng, and M. Ostendorf: Deep reinforcement learning with a natural language action space. In the 54th Annual Meeting of the Association for Computational Linguistics (ACL), 2016. [link]
  • J. He, J. Chen, X. He, J. Gao, L. Li, L. Deng, and M. Ostendorf: Deep reinforcement learning with an unbounded action space. In the International Conference on Learning Representations (ICLR), Workshop Track, 2016.
  • L. Li, H. He, and J.D. Williams: Temporal supervised learning for inferring a dialog policy from example conversations. In the IEEE Spoken Language Technology Workshop (SLT), 2014.
  • L. Li, J.D. Williams, and S. Balakrishnan: Reinforcement learning for spoken dialog management using least-squares policy iteration and fast feature selection. In the 10th Annual Conference of the International Speech Communication Association (INTERSPEECH), 2009.