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.- Anthology ID:
- 2023.findings-emnlp.739
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 11068–11081
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.739
- DOI:
- 10.18653/v1/2023.findings-emnlp.739
- Cite (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.
- Cite (Informal):
- Natural Response Generation for Chinese Reading Comprehension (Chen et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/corrections-2024-06/2023.findings-emnlp.739.pdf