Abstract
Question paraphrasing aims to restate a given question with different expressions but keep the original meaning. Recent approaches are mostly based on neural networks following a sequence-to-sequence fashion, however, these models tend to generate unpredictable results. To overcome this drawback, we propose a pipeline model based on templates. It follows three steps, a) identifies template from the input question, b) retrieves candidate templates, c) fills candidate templates with original topic words. Experiment results on two self-constructed datasets show that our model outperforms the sequence-to-sequence model in a large margin and the advantage is more promising when the size of training sample is small.- Anthology ID:
- D19-5514
- Volume:
- Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
- Venue:
- WNUT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 109–114
- Language:
- URL:
- https://aclanthology.org/D19-5514
- DOI:
- 10.18653/v1/D19-5514
- Cite (ACL):
- Yunfan Gu, Yang Yuqiao, and Zhongyu Wei. 2019. Extract, Transform and Filling: A Pipeline Model for Question Paraphrasing based on Template. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 109–114, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Extract, Transform and Filling: A Pipeline Model for Question Paraphrasing based on Template (Gu et al., WNUT 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/D19-5514.pdf