A Token-pair Framework for Information Extraction from Dialog Transcripts in SereTOD Challenge

Chenyue Wang, Xiangxing Kong, Mengzuo Huang, Feng Li, Jian Xing, Weidong Zhang, Wuhe Zou


Abstract
This paper describes our solution for Sere- TOD Challenge Track 1: Information extraction from dialog transcripts. We propose a token-pair framework to simultaneously identify entity and value mentions and link them into corresponding triples. As entity mentions are usually coreferent, we adopt a baseline model for coreference resolution. We exploit both annotated transcripts and unsupervised dialogs for training. With model ensemble and post-processing strategies, our system significantly outperforms the baseline solution and ranks first in triple f1 and third in entity f1.
Anthology ID:
2022.seretod-1.3
Volume:
Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD)
Month:
December
Year:
2022
Address:
Abu Dhabi, Beijing (Hybrid)
Editors:
Zhijian Ou, Junlan Feng, Juanzi Li
Venue:
SereTOD
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
19–23
Language:
URL:
https://aclanthology.org/2022.seretod-1.3
DOI:
10.18653/v1/2022.seretod-1.3
Bibkey:
Cite (ACL):
Chenyue Wang, Xiangxing Kong, Mengzuo Huang, Feng Li, Jian Xing, Weidong Zhang, and Wuhe Zou. 2022. A Token-pair Framework for Information Extraction from Dialog Transcripts in SereTOD Challenge. In Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD), pages 19–23, Abu Dhabi, Beijing (Hybrid). Association for Computational Linguistics.
Cite (Informal):
A Token-pair Framework for Information Extraction from Dialog Transcripts in SereTOD Challenge (Wang et al., SereTOD 2022)
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PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2022.seretod-1.3.pdf
Video:
 https://preview.aclanthology.org/ingest-acl-2023-videos/2022.seretod-1.3.mp4