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End-to-End Representation Learning for Question Answering with Weak Supervision

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Semantic Web Challenges (SemWebEval 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 769))

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Abstract

In this paper we present a knowledge base question answering system for participation in Task 4 of the QALD-7 shared task. Our system is an end-to-end neural architecture for constructing a structural semantic representation of a natural language question. We define semantic representations as graphs that are generated step-wise and can be translated into knowledge base queries to retrieve answers. We use a convolutional neural network (CNN) model to learn vector encodings for the questions and the semantic graphs and use it to select the best matching graph for the input question. We show on two different datasets that our system is able to successfully generalize to new data.

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Acknowledgments

We thank the anonymous reviewers for their valuable comments and insights that helped us to improve upon the initial version of the paper.

This work has been supported by the German Research Foundation as part of the QA-EduInf project (grant GU 798/18-1 and grant RI 803/12-1). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.

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Authors and Affiliations

  1. Ubiquitous Knowledge Processing Lab (UKP-TUDA) Department of Computer Science, Technische Universität Darmstadt, Darmstadt, Germany

    Daniil Sorokin & Iryna Gurevych

Authors
  1. Daniil Sorokin
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  2. Iryna Gurevych
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Correspondence to Daniil Sorokin .

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Editors and Affiliations

  1. Fondazione Bruno Kessler , Povo, Trento, Italy

    Mauro Dragoni

  2. University of Oxford , Oxford, United Kingdom

    Monika Solanki

  3. Linköping University , Linköping, Sweden

    Eva Blomqvist

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Sorokin, D., Gurevych, I. (2017). End-to-End Representation Learning for Question Answering with Weak Supervision. In: Dragoni, M., Solanki, M., Blomqvist, E. (eds) Semantic Web Challenges. SemWebEval 2017. Communications in Computer and Information Science, vol 769. Springer, Cham. https://doi.org/10.1007/978-3-319-69146-6_7

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  • DOI: https://doi.org/10.1007/978-3-319-69146-6_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69145-9

  • Online ISBN: 978-3-319-69146-6

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