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        <title>Natural Response Generation for Chinese Reading Comprehension</title>
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    <abstract>Machine reading comprehension (MRC) is an important area of conversation agents and draws a lot of attention. However, there is a notable limitation to current MRC benchmarks: The labeled answers are mostly either spans extracted from the target corpus or the choices of the given candidates, ignoring the natural aspect of high-quality responses. As a result, MRC models trained on these datasets can not generate human-like responses in real QA scenarios. To this end, we construct a new dataset called Penguin to promote the research of MRC, providing a training and test bed for natural response generation to real scenarios. Concretely, Penguin consists of 200k training data with high-quality fluent, and well-informed responses. Penguin is the first benchmark towards natural response generation in Chinese MRC on a relatively large scale. To address the challenges in Penguin, we develop two strong baselines: end-to-end and two-stage frameworks. Following that, we further design Prompt-BART: fine-tuning the pre-trained generative language models with a mixture of prefix prompts in Penguin. Extensive experiments validated the effectiveness of this design.</abstract>
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%0 Conference Proceedings
%T Natural Response Generation for Chinese Reading Comprehension
%A Chen, Nuo
%A Li, Hongguang
%A Bao, Yinan
%A Wang, Baoyuan
%A Li, Jia
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F chen-etal-2023-natural
%X Machine reading comprehension (MRC) is an important area of conversation agents and draws a lot of attention. However, there is a notable limitation to current MRC benchmarks: The labeled answers are mostly either spans extracted from the target corpus or the choices of the given candidates, ignoring the natural aspect of high-quality responses. As a result, MRC models trained on these datasets can not generate human-like responses in real QA scenarios. To this end, we construct a new dataset called Penguin to promote the research of MRC, providing a training and test bed for natural response generation to real scenarios. Concretely, Penguin consists of 200k training data with high-quality fluent, and well-informed responses. Penguin is the first benchmark towards natural response generation in Chinese MRC on a relatively large scale. To address the challenges in Penguin, we develop two strong baselines: end-to-end and two-stage frameworks. Following that, we further design Prompt-BART: fine-tuning the pre-trained generative language models with a mixture of prefix prompts in Penguin. Extensive experiments validated the effectiveness of this design.
%R 10.18653/v1/2023.findings-emnlp.739
%U https://aclanthology.org/2023.findings-emnlp.739
%U https://doi.org/10.18653/v1/2023.findings-emnlp.739
%P 11068-11081

Markdown (Informal)

[Natural Response Generation for Chinese Reading Comprehension](https://aclanthology.org/2023.findings-emnlp.739) (Chen et al., Findings 2023)

• Natural Response Generation for Chinese Reading Comprehension (Chen et al., Findings 2023)

ACL

• Nuo Chen, Hongguang Li, Yinan Bao, Baoyuan Wang, and Jia Li. 2023. Natural Response Generation for Chinese Reading Comprehension. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 11068–11081, Singapore. Association for Computational Linguistics.