Abstract
In this study, we tackle the challenge of inadequate and costly training data that has hindered the development of conversational question answering (ConvQA) systems. Enterprises have a large corpus of diverse internal documents. Instead of relying on a searching engine, a more compelling approach for people to comprehend these documents is to create a dialogue system. In this paper, we propose a robust dialog synthesising method. We learn the segmentation of data for the dialog task instead of using segmenting at sentence boundaries. The synthetic dataset generated by our proposed method achieves superior quality when compared to WikiDialog, as assessed through machine and human evaluations. By employing our inpainted data for ConvQA retrieval system pre-training, we observed a notable improvement in performance across OR-QuAC benchmarks.- Anthology ID:
- 2024.findings-acl.477
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8012–8026
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.477
- DOI:
- 10.18653/v1/2024.findings-acl.477
- Cite (ACL):
- Fanyou Wu, Weijie Xu, Chandan Reddy, and Srinivasan Sengamedu. 2024. Synthesizing Conversations from Unlabeled Documents using Automatic Response Segmentation. In Findings of the Association for Computational Linguistics: ACL 2024, pages 8012–8026, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Synthesizing Conversations from Unlabeled Documents using Automatic Response Segmentation (Wu et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.findings-acl.477.pdf