@inproceedings{wang-etal-2018-deep,
    title = "Deep Reinforcement Learning for {NLP}",
    author = "Wang, William Yang  and
      Li, Jiwei  and
      He, Xiaodong",
    editor = "Artzi, Yoav  and
      Eisenstein, Jacob",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/P18-5007/",
    doi = "10.18653/v1/P18-5007",
    pages = "19--21",
    abstract = "Many Natural Language Processing (NLP) tasks (including generation, language grounding, reasoning, information extraction, coreference resolution, and dialog) can be formulated as deep reinforcement learning (DRL) problems. However, since language is often discrete and the space for all sentences is infinite, there are many challenges for formulating reinforcement learning problems of NLP tasks. In this tutorial, we provide a gentle introduction to the foundation of deep reinforcement learning, as well as some practical DRL solutions in NLP. We describe recent advances in designing deep reinforcement learning for NLP, with a special focus on generation, dialogue, and information extraction. Finally, we discuss why they succeed, and when they may fail, aiming at providing some practical advice about deep reinforcement learning for solving real-world NLP problems."
}