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        <namePart type="given">Po-Nien</namePart>
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    <abstract>Extracting rationales can help human understand which information the model utilizes and how it makes the prediction towards better interpretability. However, annotating rationales requires much effort and only few datasets contain such labeled rationales, making supervised learning for rationalization difficult. In this paper, we propose a novel approach that leverages the benefits of both multi-task learning and transfer learning for generating rationales through question answering in a zero-shot fashion. For two benchmark rationalization datasets, the proposed method achieves comparable or even better performance of rationalization without any supervised signal, demonstrating the great potential of zero-shot rationalization for better interpretability.</abstract>
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%0 Conference Proceedings
%T Zero-Shot Rationalization by Multi-Task Transfer Learning from Question Answering
%A Kung, Po-Nien
%A Yang, Tse-Hsuan
%A Chen, Yi-Cheng
%A Yin, Sheng-Siang
%A Chen, Yun-Nung
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F kung-etal-2020-zero
%X Extracting rationales can help human understand which information the model utilizes and how it makes the prediction towards better interpretability. However, annotating rationales requires much effort and only few datasets contain such labeled rationales, making supervised learning for rationalization difficult. In this paper, we propose a novel approach that leverages the benefits of both multi-task learning and transfer learning for generating rationales through question answering in a zero-shot fashion. For two benchmark rationalization datasets, the proposed method achieves comparable or even better performance of rationalization without any supervised signal, demonstrating the great potential of zero-shot rationalization for better interpretability.
%R 10.18653/v1/2020.findings-emnlp.198
%U https://aclanthology.org/2020.findings-emnlp.198
%U https://doi.org/10.18653/v1/2020.findings-emnlp.198
%P 2187-2197

Markdown (Informal)

[Zero-Shot Rationalization by Multi-Task Transfer Learning from Question Answering](https://aclanthology.org/2020.findings-emnlp.198) (Kung et al., Findings 2020)

• Zero-Shot Rationalization by Multi-Task Transfer Learning from Question Answering (Kung et al., Findings 2020)

ACL

• Po-Nien Kung, Tse-Hsuan Yang, Yi-Cheng Chen, Sheng-Siang Yin, and Yun-Nung Chen. 2020. Zero-Shot Rationalization by Multi-Task Transfer Learning from Question Answering. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2187–2197, Online. Association for Computational Linguistics.