@inproceedings{wang-etal-2022-token,
title = "A Token-pair Framework for Information Extraction from Dialog Transcripts in {S}ere{TOD} Challenge",
author = "Wang, Chenyue and
Kong, Xiangxing and
Huang, Mengzuo and
Li, Feng and
Xing, Jian and
Zhang, Weidong and
Zou, Wuhe",
editor = "Ou, Zhijian and
Feng, Junlan and
Li, Juanzi",
booktitle = "Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD)",
month = dec,
year = "2022",
address = "Abu Dhabi, Beijing (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.seretod-1.3",
doi = "10.18653/v1/2022.seretod-1.3",
pages = "19--23",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T A Token-pair Framework for Information Extraction from Dialog Transcripts in SereTOD Challenge
%A Wang, Chenyue
%A Kong, Xiangxing
%A Huang, Mengzuo
%A Li, Feng
%A Xing, Jian
%A Zhang, Weidong
%A Zou, Wuhe
%Y Ou, Zhijian
%Y Feng, Junlan
%Y Li, Juanzi
%S Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, Beijing (Hybrid)
%F wang-etal-2022-token
%X 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.
%R 10.18653/v1/2022.seretod-1.3
%U https://aclanthology.org/2022.seretod-1.3
%U https://doi.org/10.18653/v1/2022.seretod-1.3
%P 19-23
Markdown (Informal)
[A Token-pair Framework for Information Extraction from Dialog Transcripts in SereTOD Challenge](https://aclanthology.org/2022.seretod-1.3) (Wang et al., SereTOD 2022)
ACL