Preferred Answer Selection in Stack Overflow: Better Text Representations ... and Metadata, Metadata, Metadata
Steven Xu, Andrew Bennett, Doris Hoogeveen, Jey Han Lau, Timothy Baldwin
Abstract
Community question answering (cQA) forums provide a rich source of data for facilitating non-factoid question answering over many technical domains. Given this, there is considerable interest in answer retrieval from these kinds of forums. However this is a difficult task as the structure of these forums is very rich, and both metadata and text features are important for successful retrieval. While there has recently been a lot of work on solving this problem using deep learning models applied to question/answer text, this work has not looked at how to make use of the rich metadata available in cQA forums. We propose an attention-based model which achieves state-of-the-art results for text-based answer selection alone, and by making use of complementary meta-data, achieves a substantially higher result over two reference datasets novel to this work.- Anthology ID:
- W18-6119
- Volume:
- Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text
- Month:
- November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
- Venue:
- WNUT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 137–147
- Language:
- URL:
- https://aclanthology.org/W18-6119
- DOI:
- 10.18653/v1/W18-6119
- Cite (ACL):
- Steven Xu, Andrew Bennett, Doris Hoogeveen, Jey Han Lau, and Timothy Baldwin. 2018. Preferred Answer Selection in Stack Overflow: Better Text Representations ... and Metadata, Metadata, Metadata. In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, pages 137–147, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Preferred Answer Selection in Stack Overflow: Better Text Representations … and Metadata, Metadata, Metadata (Xu et al., WNUT 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W18-6119.pdf