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Deep Learning in Question Answering

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Deep Learning in Natural Language Processing

Abstract

Question answering (QA) is a challenging task in natural language processing. Recently, with the remarkable success of deep learning on many natural language processing tasks, including semantic and syntactic analysis, machine translation, relation extraction, etc., more and more efforts have also been devoted to the task of question answering. This chapter briefly introduces the recent advances in deep learning methods on two typical and popular question answering tasks. (1) Deep learning in question answering over knowledge base (KBQA) which mainly employs deep neural networks to understand the meaning of the questions and try to translate them into structured queries, or directly translate them into distributional semantic representations compared with candidate answers in the knowledge base. (2) Deep learning in machine comprehension (MC) which manages to construct an end-to-end paradigm based on novel neural networks for directly computing the deep semantic matching among question, answers and the given passage.

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Notes

  1. 1.

    A CVT node is usually not a real-world entity, but often refers to an event, e.g., a marriage event, or a presidency event, which can represent an entry of data with multiple fields.

  2. 2.

    More details can be found in https://nlp.stanford.edu/software/sempre/.

  3. 3.

    Obtained through https://www.microsoft.com/en-us/download/details.aspx?id=52763.

  4. 4.

    Obtained through http://beforethecode.com/projects/omdb/download.aspx.

  5. 5.

    Obtained through http://grouplens.org/datasets/movielens/.

  6. 6.

    http://qald.sebastianwalter.org/.

  7. 7.

    https://datasets.maluuba.com/NewsQA.

  8. 8.

    http://www.msmarco.org.

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Author information

Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Kang Liu

  2. Peking University, Beijing, China

    Yansong Feng

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  1. Kang Liu
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  2. Yansong Feng
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Correspondence to Kang Liu .

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Editors and Affiliations

  1. AI Research at Citadel , Chicago, Illinois, USA

    Li Deng

  2. Tsinghua University , Beijing, China

    Yang Liu

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Liu, K., Feng, Y. (2018). Deep Learning in Question Answering. In: Deng, L., Liu, Y. (eds) Deep Learning in Natural Language Processing. Springer, Singapore. https://doi.org/10.1007/978-981-10-5209-5_7

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  • DOI: https://doi.org/10.1007/978-981-10-5209-5_7

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  • Online ISBN: 978-981-10-5209-5

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