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        <title>Improving the Robustness of Question Answering Systems to Question Paraphrasing</title>
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    <abstract>Despite the advancement of question answering (QA) systems and rapid improvements on held-out test sets, their generalizability is a topic of concern. We explore the robustness of QA models to question paraphrasing by creating two test sets consisting of paraphrased SQuAD questions. Paraphrased questions from the first test set are very similar to the original questions designed to test QA models’ over-sensitivity, while questions from the second test set are paraphrased using context words near an incorrect answer candidate in an attempt to confuse QA models. We show that both paraphrased test sets lead to significant decrease in performance on multiple state-of-the-art QA models. Using a neural paraphrasing model trained to generate multiple paraphrased questions for a given source question and a set of paraphrase suggestions, we propose a data augmentation approach that requires no human intervention to re-train the models for improved robustness to question paraphrasing.</abstract>
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%0 Conference Proceedings
%T Improving the Robustness of Question Answering Systems to Question Paraphrasing
%A Gan, Wee Chung
%A Ng, Hwee Tou
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F gan-ng-2019-improving
%X Despite the advancement of question answering (QA) systems and rapid improvements on held-out test sets, their generalizability is a topic of concern. We explore the robustness of QA models to question paraphrasing by creating two test sets consisting of paraphrased SQuAD questions. Paraphrased questions from the first test set are very similar to the original questions designed to test QA models’ over-sensitivity, while questions from the second test set are paraphrased using context words near an incorrect answer candidate in an attempt to confuse QA models. We show that both paraphrased test sets lead to significant decrease in performance on multiple state-of-the-art QA models. Using a neural paraphrasing model trained to generate multiple paraphrased questions for a given source question and a set of paraphrase suggestions, we propose a data augmentation approach that requires no human intervention to re-train the models for improved robustness to question paraphrasing.
%R 10.18653/v1/P19-1610
%U https://aclanthology.org/P19-1610
%U https://doi.org/10.18653/v1/P19-1610
%P 6065-6075

Markdown (Informal)

[Improving the Robustness of Question Answering Systems to Question Paraphrasing](https://aclanthology.org/P19-1610) (Gan & Ng, ACL 2019)

• Improving the Robustness of Question Answering Systems to Question Paraphrasing (Gan & Ng, ACL 2019)

ACL

• Wee Chung Gan and Hwee Tou Ng. 2019. Improving the Robustness of Question Answering Systems to Question Paraphrasing. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6065–6075, Florence, Italy. Association for Computational Linguistics.