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    <abstract>Advances in Question Answering (QA) research require additional datasets for new domains, languages, and types of questions, as well as for performance increases. Human creation of a QA dataset like SQuAD, however, is expensive. As an alternative, an unsupervised QA approach has been proposed so that QA training data can be generated automatically. However, the performance of unsupervised QA is much lower than that of supervised QA models. We identify two anomalies in the automatically generated questions and propose how they can be mitigated. We show our approach helps improve unsupervised QA significantly across a number of QA tasks.</abstract>
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%0 Conference Proceedings
%T Handling Anomalies of Synthetic Questions in Unsupervised Question Answering
%A Hong, Giwon
%A Kang, Junmo
%A Lim, Doyeon
%A Myaeng, Sung-Hyon
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F hong-etal-2020-handling
%X Advances in Question Answering (QA) research require additional datasets for new domains, languages, and types of questions, as well as for performance increases. Human creation of a QA dataset like SQuAD, however, is expensive. As an alternative, an unsupervised QA approach has been proposed so that QA training data can be generated automatically. However, the performance of unsupervised QA is much lower than that of supervised QA models. We identify two anomalies in the automatically generated questions and propose how they can be mitigated. We show our approach helps improve unsupervised QA significantly across a number of QA tasks.
%R 10.18653/v1/2020.coling-main.306
%U https://aclanthology.org/2020.coling-main.306
%U https://doi.org/10.18653/v1/2020.coling-main.306
%P 3441-3448

Markdown (Informal)

[Handling Anomalies of Synthetic Questions in Unsupervised Question Answering](https://aclanthology.org/2020.coling-main.306) (Hong et al., COLING 2020)

• Handling Anomalies of Synthetic Questions in Unsupervised Question Answering (Hong et al., COLING 2020)

ACL

• Giwon Hong, Junmo Kang, Doyeon Lim, and Sung-Hyon Myaeng. 2020. Handling Anomalies of Synthetic Questions in Unsupervised Question Answering. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3441–3448, Barcelona, Spain (Online). International Committee on Computational Linguistics.