@inproceedings{j-wang-etal-2022-globalpointer,
title = "A {G}lobal{P}ointer based Robust Approach for Information Extraction from Dialog Transcripts",
author = "Wang, Yanbo J. and
Chen, Sheng and
Cai, Hengxing and
Wei, Wei and
Yan, Kuo and
Sun, Zhe and
Qin, Hui and
Li, Yuming and
Cai, Xiaochen",
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-1.2",
doi = "10.18653/v1/2022.seretod-1.2",
pages = "13--18",
abstract = "With the widespread popularisation of intelligent technology, task-based dialogue systems (TOD) are increasingly being applied to a wide variety of practical scenarios. As the key tasks in dialogue systems, named entity recognition and slot filling play a crucial role in the completeness and accuracy of information extraction. This paper is an evaluation paper for Sere-TOD 2022 Workshop challenge (Track 1 Information extraction from dialog transcripts). We proposed a multi-model fusion approach based on GlobalPointer, combined with some optimisation tricks, finally achieved an entity F1 of 60.73, an entity-slot-value triple F1 of 56, and an average F1 of 58.37, and got the highest score in SereTOD 2022 Workshop challenge",
}
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<abstract>With the widespread popularisation of intelligent technology, task-based dialogue systems (TOD) are increasingly being applied to a wide variety of practical scenarios. As the key tasks in dialogue systems, named entity recognition and slot filling play a crucial role in the completeness and accuracy of information extraction. This paper is an evaluation paper for Sere-TOD 2022 Workshop challenge (Track 1 Information extraction from dialog transcripts). We proposed a multi-model fusion approach based on GlobalPointer, combined with some optimisation tricks, finally achieved an entity F1 of 60.73, an entity-slot-value triple F1 of 56, and an average F1 of 58.37, and got the highest score in SereTOD 2022 Workshop challenge</abstract>
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%0 Conference Proceedings
%T A GlobalPointer based Robust Approach for Information Extraction from Dialog Transcripts
%A Wang, Yanbo J.
%A Chen, Sheng
%A Cai, Hengxing
%A Wei, Wei
%A Yan, Kuo
%A Sun, Zhe
%A Qin, Hui
%A Li, Yuming
%A Cai, Xiaochen
%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 j-wang-etal-2022-globalpointer
%X With the widespread popularisation of intelligent technology, task-based dialogue systems (TOD) are increasingly being applied to a wide variety of practical scenarios. As the key tasks in dialogue systems, named entity recognition and slot filling play a crucial role in the completeness and accuracy of information extraction. This paper is an evaluation paper for Sere-TOD 2022 Workshop challenge (Track 1 Information extraction from dialog transcripts). We proposed a multi-model fusion approach based on GlobalPointer, combined with some optimisation tricks, finally achieved an entity F1 of 60.73, an entity-slot-value triple F1 of 56, and an average F1 of 58.37, and got the highest score in SereTOD 2022 Workshop challenge
%R 10.18653/v1/2022.seretod-1.2
%U https://aclanthology.org/2022.seretod-1.2
%U https://doi.org/10.18653/v1/2022.seretod-1.2
%P 13-18
Markdown (Informal)
[A GlobalPointer based Robust Approach for Information Extraction from Dialog Transcripts](https://aclanthology.org/2022.seretod-1.2) (Wang et al., SereTOD 2022)
ACL
- Yanbo J. Wang, Sheng Chen, Hengxing Cai, Wei Wei, Kuo Yan, Zhe Sun, Hui Qin, Yuming Li, and Xiaochen Cai. 2022. A GlobalPointer based Robust Approach for Information Extraction from Dialog Transcripts. In Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD), pages 13–18, Abu Dhabi, Beijing (Hybrid). Association for Computational Linguistics.