Abstract
Question Answering (QA) is a critical NLP task mainly based on deep learning models that allow users to answer questions in natural language and get a response. Since available general-purpose datasets are often not effective enough to suitably train a QA model, one of the main problems in this context is related to the availability of datasets which fit the considered context. Moreover, such datasets are generally in English, making QA system design in different languages difficult. To alleviate the above-depicted issues, in this work, we propose a framework which automatically generates a dataset for a given language and a given topic. To train our system in any language, an alternative way to evaluate the quality of the answers is needed, so we propose a novel unsupervised method. To test the proposed technique, we generate a dataset for the topic “computer science” and the language “Italian” and compare the performance of a QA system trained on available datasets and the built one.
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References
Beltagy, I., Cohan, A., Lo, K.: Scibert: Pretrained contextualized embeddings for scientific text. arXiv preprint arXiv:1903.10676, 1(1.3), 8 (2019)
Choi, E., et al.: QUAC: question answering in context. arXiv preprint arXiv:1808.07036 (2018)
Croce, D., Zelenanska, A., Basili, R.: Neural learning for question answering in Italian. In: Ghidini, C., Magnini, B., Passerini, A., Traverso, P. (eds.) AI*IA 2018. LNCS (LNAI), vol. 11298, pp. 389–402. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03840-3_29
Joshi, M., Choi, E., Weld, D.S., Zettlemoyer, L.: Triviaqa: a large scale distantly supervised challenge dataset for reading comprehension. arXiv preprint arXiv:1705.03551 (2017)
Maia, M., et al.: Www 2018 open challenge: financial opinion mining and question answering (2018)
Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019)
Rajpurkar, P., Jia, R., Liang, P.: Know what you don’t know: unanswerable questions for squad. arXiv preprint arXiv:1806.03822 (2018)
Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250 (2016)
Reddy, S., Chen, D., Manning, C.D.: Coqa: a conversational question answering challenge. TACL 7, 249–266 (2019)
Kǒ ciský, T., et al.: The NarrativeQA reading comprehension challenge. TACL, TBD:TBD (2018)
Adam Trischler, et al.: Newsqa: a machine comprehension dataset. arXiv preprint arXiv:1611.09830 (2016)
van Aken, B., Winter, B., Löser, A., Gers, F.A.: How does Bert answer questions? A layer-wise analysis of transformer representations. In: CIKM, pp. 1823–1832 (2019)
Wang, W., Wei, F., Dong, L., Bao, H., Yang, N., Zhou, M.: Minilm: deep self-attention distillation for task-agnostic compression of pre-trained transformers. arXiv preprint arXiv:2002.10957 (2020)
Wolf, T.: Transformers: State-of-the-Art Natural Language Processing
Xiong, W., et al.: Tweetqa: a social media focused question answering dataset. arXiv preprint arXiv:1907.06292 (2019)
Yang, Y., Yih, W., Meek, C.: Wikiqa: a challenge dataset for open-domain question answering. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2013–2018 (2015)
Zhong, H., Xiao, C., Tu, C., Zhang, T., Liu, Z., Sun, M.: JEC-QA: a legal-domain question answering dataset. arXiv preprint arXiv:1911.12011 (2019)
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Angiulli, F., Del Prete, A., Fassetti, F., Nisticò, S. (2022). A Semi-automatic Data Generator for Query Answering. In: Ceci, M., Flesca, S., Masciari, E., Manco, G., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2022. Lecture Notes in Computer Science(), vol 13515. Springer, Cham. https://doi.org/10.1007/978-3-031-16564-1_11
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DOI: https://doi.org/10.1007/978-3-031-16564-1_11
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