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Improving Answer Type Classification Quality Through Combined Question Answering Datasets

  • Conference paper
  • First Online:
Knowledge Science, Engineering and Management (KSEM 2021)

Abstract

Understanding what a person is asking via a question is one of the first steps that humans use to find the corresponding answer. The same is true for Question Answering (QA) systems. Hence, the quality of the expected answer type classifier (EAT) has a direct influence on QA quality. Many research papers are aiming at improving short text classification quality, however, there is a lack of focus on the impact of training data characteristics on the classification quality as well as effective reuse of datasets through their augmentation and combination. In this work, we propose an approach of analyzing and improving the EAT classification quality via a combination of existing QA datasets. We provide 4 new question classification datasets based on several well-known QA datasets as well as the approach to unify its class taxonomy. We made a sufficient amount of experiments to demonstrate several valuable insights related to the impact of training data characteristics on the classification quality. Additionally, an embedding-based approach for automatic data labeling error detection is demonstrated.

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Notes

  1. 1.

    https://developers.google.com/freebase/.

  2. 2.

    https://wiki.dbpedia.org/services-resources/ontology.

  3. 3.

    https://huggingface.co/models.

  4. 4.

    https://github.com/Perevalov/eat_classification_ksem2021.

  5. 5.

    The complete experimental results are available as online appendix at footnote 4.

  6. 6.

    The full list of questions considered as wrongly labeled is available at footnote 4.

References

  1. Berant, J., Chou, A., Frostig, R., Liang, P.: Semantic parsing on freebase from question-answer pairs. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1533–1544 (2013)

    Google Scholar 

  2. Bordes, A., Usunier, N., Chopra, S., Weston, J.: Large-scale simple question answering with memory networks. arXiv preprint arXiv:1506.02075 (2015)

  3. Diefenbach, D., Lopez, V., Singh, K., Maret, P.: Core techniques of question answering systems over knowledge bases: a survey. Knowl. Inf. Syst. 55(3), 529–569 (2017). https://doi.org/10.1007/s10115-017-1100-y

    Article  Google Scholar 

  4. Diefenbach, D., Singh, K., Both, A., Cherix, D., Lange, C., Auer, S.: The Qanary ecosystem: getting new insights by composing question answering pipelines. In: Cabot, J., De Virgilio, R., Torlone, R. (eds.) ICWE 2017. LNCS, vol. 10360, pp. 171–189. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60131-1_10

    Chapter  Google Scholar 

  5. Dubey, M., Banerjee, D., Chaudhuri, D., Lehmann, J.: EARL: joint entity and relation linking for question answering over knowledge graphs. In: Vrandečić, D., et al. (eds.) ISWC 2018. LNCS, vol. 11136, pp. 108–126. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00671-6_7

    Chapter  Google Scholar 

  6. Garigliotti, D., Hasibi, F., Balog, K.: Target type identification for entity-bearing queries. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (2017)

    Google Scholar 

  7. Höffner, K., Walter, S., Marx, E., Usbeck, R., Lehmann, J., Ngonga Ngomo, A.C.: Survey on challenges of question answering in the semantic web. Seman. Web 8(6), 895–920 (2017)

    Article  Google Scholar 

  8. Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Melbourne, Australia, pp. 328–339. Association for Computational Linguistics (July 2018). https://doi.org/10.18653/v1/P18-1031

  9. Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pp. 427–431. Association for Computational Linguistics, Valencia, Spain (April 2017), https://www.aclweb.org/anthology/E17-2068

  10. Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Baltimore, Maryland, pp. 655–665. Association for Computational Linguistics (June 2014). https://doi.org/10.3115/v1/P14-1062

  11. Kamath, S., Grau, B., Ma, Y.: Verification of the expected answer type for biomedical question answering. In: 2018 Companion Proceedings of the The Web Conference (2018)

    Google Scholar 

  12. Kamath, S., Grau, B., Ma, Y.: Predicting and integrating expected answer types into a simple recurrent neural network model for answer sentence selection. Computación y Sistemas 23(2019)

    Google Scholar 

  13. Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751. Association for Computational Linguistics, Doha, Qatar (2014). https://doi.org/10.3115/v1/D14-1181

  14. Li, X., Roth, D.: Learning question classifiers. In: The 19th International Conference on Computational Linguistics, COLING 2002 (2002)

    Google Scholar 

  15. Lukovnikov, D., Fischer, A., Lehmann, J., Auer, S.: Neural network-based question answering over knowledge graphs on word and character level. In: Proceedings of the 26th International Conference on World Wide Web, WWW ’17, Republic and Canton of Geneva, CHE, pp. 1211–1220. IW3C2 (2017). https://doi.org/10.1145/3038912.3052675

  16. Marivate, V., Sefara, T.: Improving short text classification through global augmentation methods. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-MAKE 2020. LNCS, vol. 12279, pp. 385–399. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_21

    Chapter  Google Scholar 

  17. Mihindukulasooriya, N., Dubey, M., Gliozzo, A., Lehmann, J., Ngomo, A.C.N., Usbeck, R.: SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge. CoRR/arXiv abs/2012.00555 (2020). https://arxiv.org/abs/2012.00555

  18. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 26. Curran Associates, Inc. (2013). https://proceedings.neurips.cc/paper/2013/file/9aa42b31882ec039965f3c4923ce901b-Paper.pdf

  19. Nikas, C., Fafalios, P., Tzitzikas, Y.: Two-stage semantic answer type prediction for question answering using BERT and class-specificity rewarding. In: Proceedings of the SeMantic AnsweR Type prediction task (SMART), ISWC 2020. CEUR Workshop Proceedings, vol. 2774, pp. 19–28. CEUR-WS.org (2020). http://ceur-ws.org/Vol-2774/paper-03.pdf

  20. Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), , Doha, Qatar, pp. 1532–1543. Association for Computational Linguistics (October 2014). https://doi.org/10.3115/v1/D14-1162

  21. Perevalov, A., Both, A.: Augmentation-based answer type classification of the SMART dataset. In: Proceedings of the SeMantic AnsweR Type prediction task (SMART), ISWC 2020. CEUR Workshop Proceedings, vol. 2774, pp. 1–9. CEUR-WS.org (2020). http://ceur-ws.org/Vol-2774/paper-01.pdf

  22. Peters, M., et al.: Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), New Orleans, Louisiana, pp. 2227–2237. Association for Computational Linguistics (June 2018). https://doi.org/10.18653/v1/N18-1202

  23. Setty, V., Balog, K.: Semantic answer type prediction using BERT IAI at the ISWC SMART task 2020. In: Proceedings of the SeMantic AnsweR Type prediction task (SMART), ISWC 2020. CEUR Workshop Proceedings, vol. 2774, pp. 10–18. CEUR-WS.org (2020). http://ceur-ws.org/Vol-2774/paper-02.pdf

  24. Singh, K., et al.: Why reinvent the wheel: let’s build question answering systems together. In: Proceedings of the 2018 World Wide Web Conference. pp. 1247–1256, WWW ’18. International World Wide Web Conferences Steering Committee (2018). https://doi.org/10.1145/3178876.3186023

  25. Steinmetz, N., Sattler, K.: COALA - a rule-based approach to answer type prediction. In: Proceedings of the SeMantic AnsweR Type prediction task (SMART), ISWC 2020. CEUR Workshop Proceedings, vol. 2774, pp. 29–40. CEUR-WS.org (2020). http://ceur-ws.org/Vol-2774/paper-04.pdf

  26. Sun, C., Qiu, X., Xu, Y., Huang, X.: How to fine-tune BERT for text classification? arXiv abs/1905.05583 (2019)

    Google Scholar 

  27. Trivedi, P., Maheshwari, G., Dubey, M., Lehmann, J.: LC-QuAD: a corpus for complex question answering over knowledge graphs. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10588, pp. 210–218. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68204-4_22

    Chapter  Google Scholar 

  28. Usbeck, R., Gusmita, R.H., Ngomo, A.N., Saleem, M.: 9th challenge on question answering over linked data (QALD-9) (invited paper). In: Joint proceedings of the 4th Workshop on Semantic Deep Learning (SemDeep-4) and NLIWoD4: Natural Language Interfaces for the Web of Data (NLIWOD-4) and 9th Question Answering over Linked Data challenge (QALD-9) co-located with 17th International Semantic Web Conference (ISWC 2018), Monterey, California, United States of America, 8–9 October 2018, vol. 2241, pp. 58–64. CEUR Workshop Proceedings (2018)

    Google Scholar 

  29. Wang, J., Wang, Z., Zhang, D., Yan, J.: Combining knowledge with deep convolutional neural networks for short text classification. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, IJCAI-17, vol. 350, pp. 2915–2921 (2017). https://doi.org/10.24963/ijcai.2017/406

  30. Wen, T.H., et al.: A network-based end-to-end trainable task-oriented dialogue system. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, Valencia, Spain, pp. 438–449. Association for Computational Linguistics (April 2017). https://www.aclweb.org/anthology/E17-1042

  31. Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., Le, Q.V.: XLNet: generalized autoregressive pretraining for language understanding. In: Advances in Neural Information Processing Systems, vol. 32. Curran Associates, Inc. (2019)

    Google Scholar 

  32. Yavuz, S., Gur, I., Su, Y., Srivatsa, M., Yan, X.: Improving semantic parsing via answer type inference. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, Texas, pp. 149–159. Association for Computational Linguistics (November 2016). https://doi.org/10.18653/v1/D16-1015

  33. Yu, S., Su, J., Luo, D.: Improving BERT-Based text classification with auxiliary sentence and domain knowledge. IEEE Access 7, 176600–176612 (2019). https://doi.org/10.1109/ACCESS.2019.2953990

    Article  Google Scholar 

  34. Zhang, H., Zhong, G.: Improving short text classification by learning vector representations of both words and hidden topics. Knowl. Based Syst. 102, 76–86 (2016). https://doi.org/10.1016/j.knosys.2016.03.027

    Article  Google Scholar 

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Correspondence to Aleksandr Perevalov .

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Perevalov, A., Both, A. (2021). Improving Answer Type Classification Quality Through Combined Question Answering Datasets. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management . KSEM 2021. Lecture Notes in Computer Science(), vol 12816. Springer, Cham. https://doi.org/10.1007/978-3-030-82147-0_16

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  • DOI: https://doi.org/10.1007/978-3-030-82147-0_16

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