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A FAQ Search Training Method Based on Automatically Generated Questions

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Information Retrieval Technology (AIRS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11292))

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Abstract

We propose a FAQ search method with automatically generated questions by a question generator created from community Q&As. In our method, a search model is trained with automatically generated questions and their corresponding FAQs. We conducted experiments on a Japanese Q&A dataset created from a user support service on Twitter. The proposed method showed better Mean Reciprocal Rank and Recall@1 than a FAQ ranking model trained with the same community Q&As.

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Notes

  1. 1.

    https://chiebukuro.yahoo.co.jp/.

  2. 2.

    https://answers.yahoo.com/.

  3. 3.

    http://taku910.github.io/mecab/.

References

  1. Bai, B., et al.: Supervised semantic indexing. In: Proceedings of CIKM 2009 (2009)

    Google Scholar 

  2. Dong, L., Mallinson, J., Reddy, S., Lapata, M.: Learning to paraphrase for question answering. In: Proceedings of EMNLP 2017, pp. 875–886 (2017)

    Google Scholar 

  3. Duan, N., Tang, D., Chen, P., Zhou, M.: Question generation for question answering. In: Proceedings of EMNLP 2017, pp. 866–874. Association for Computational Linguistics (2017)

    Google Scholar 

  4. Luong, T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: Proceedings of EMNLP 2015, pp. 1412–1421. Association for Computational Linguistics (2015)

    Google Scholar 

  5. Makino, T., Noro, T., Iwakura, T.: An FAQ search method using a document classifier trained with automatically generated training data. In: Booth, R., Zhang, M.-L. (eds.) PRICAI 2016. LNCS (LNAI), vol. 9810, pp. 295–305. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42911-3_25

    Chapter  Google Scholar 

  6. Serban, I.V., et al.: Generating factoid questions with recurrent neural networks: The 30m factoid question-answer corpus. In: Proceedings of ACL 2016, pp. 588–598. Association for Computational Linguistics (2016)

    Google Scholar 

  7. Surdeanu, M., Ciaramita, M., Zaragoza, H.: Learning to rank answers on large online QA collections. In: Proceedings of ACL 2008, pp. 719–727 (2008)

    Google Scholar 

  8. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Proceedings of NIPS 2014, pp. 3104–3112 (2014)

    Google Scholar 

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Correspondence to Takuya Makino .

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Makino, T., Noro, T., Yoshikawa, H., Iwakura, T., Sekine, S., Inui, K. (2018). A FAQ Search Training Method Based on Automatically Generated Questions. In: Tseng, YH., et al. Information Retrieval Technology. AIRS 2018. Lecture Notes in Computer Science(), vol 11292. Springer, Cham. https://doi.org/10.1007/978-3-030-03520-4_7

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  • DOI: https://doi.org/10.1007/978-3-030-03520-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03519-8

  • Online ISBN: 978-3-030-03520-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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