Abstract
We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE, aiming to support the prediction of relation(s) between two arguments that appear in a dialogue. We further offer DialogRE as a platform for studying cross-sentence RE as most facts span multiple sentences. We argue that speaker-related information plays a critical role in the proposed task, based on an analysis of similarities and differences between dialogue-based and traditional RE tasks. Considering the timeliness of communication in a dialogue, we design a new metric to evaluate the performance of RE methods in a conversational setting and investigate the performance of several representative RE methods on DialogRE. Experimental results demonstrate that a speaker-aware extension on the best-performing model leads to gains in both the standard and conversational evaluation settings. DialogRE is available at https://dataset.org/dialogre/.- Anthology ID:
- 2020.acl-main.444
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4927–4940
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.444
- DOI:
- 10.18653/v1/2020.acl-main.444
- Cite (ACL):
- Dian Yu, Kai Sun, Claire Cardie, and Dong Yu. 2020. Dialogue-Based Relation Extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4927–4940, Online. Association for Computational Linguistics.
- Cite (Informal):
- Dialogue-Based Relation Extraction (Yu et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2020.acl-main.444.pdf
- Code
- additional community code
- Data
- DialogRE, DocRED, KnowledgeNet