@inproceedings{lee-etal-2023-autotrigger,
title = "{A}uto{T}rigg{ER}: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction",
author = "Lee, Dong-Ho and
Selvam, Ravi Kiran and
Sarwar, Sheikh Muhammad and
Lin, Bill Yuchen and
Morstatter, Fred and
Pujara, Jay and
Boschee, Elizabeth and
Allan, James and
Ren, Xiang",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.219",
doi = "10.18653/v1/2023.eacl-main.219",
pages = "3011--3025",
abstract = "Deep neural models for named entity recognition (NER) have shown impressive results in overcoming label scarcity and generalizing to unseen entities by leveraging distant supervision and auxiliary information such as explanations. However, the costs of acquiring such additional information are generally prohibitive. In this paper, we present a novel two-stage framework (AutoTriggER) to improve NER performance by automatically generating and leveraging {``}entity triggers{''} which are human-readable cues in the text that help guide the model to make better decisions. Our framework leverages post-hoc explanation to generate rationales and strengthens a model{'}s prior knowledge using an embedding interpolation technique. This approach allows models to exploit triggers to infer entity boundaries and types instead of solely memorizing the entity words themselves. Through experiments on three well-studied NER datasets, AutoTriggER shows strong label-efficiency, is capable of generalizing to unseen entities, and outperforms the RoBERTa-CRF baseline by nearly 0.5 F1 points on average.",
}
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<abstract>Deep neural models for named entity recognition (NER) have shown impressive results in overcoming label scarcity and generalizing to unseen entities by leveraging distant supervision and auxiliary information such as explanations. However, the costs of acquiring such additional information are generally prohibitive. In this paper, we present a novel two-stage framework (AutoTriggER) to improve NER performance by automatically generating and leveraging “entity triggers” which are human-readable cues in the text that help guide the model to make better decisions. Our framework leverages post-hoc explanation to generate rationales and strengthens a model’s prior knowledge using an embedding interpolation technique. This approach allows models to exploit triggers to infer entity boundaries and types instead of solely memorizing the entity words themselves. Through experiments on three well-studied NER datasets, AutoTriggER shows strong label-efficiency, is capable of generalizing to unseen entities, and outperforms the RoBERTa-CRF baseline by nearly 0.5 F1 points on average.</abstract>
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%0 Conference Proceedings
%T AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction
%A Lee, Dong-Ho
%A Selvam, Ravi Kiran
%A Sarwar, Sheikh Muhammad
%A Lin, Bill Yuchen
%A Morstatter, Fred
%A Pujara, Jay
%A Boschee, Elizabeth
%A Allan, James
%A Ren, Xiang
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F lee-etal-2023-autotrigger
%X Deep neural models for named entity recognition (NER) have shown impressive results in overcoming label scarcity and generalizing to unseen entities by leveraging distant supervision and auxiliary information such as explanations. However, the costs of acquiring such additional information are generally prohibitive. In this paper, we present a novel two-stage framework (AutoTriggER) to improve NER performance by automatically generating and leveraging “entity triggers” which are human-readable cues in the text that help guide the model to make better decisions. Our framework leverages post-hoc explanation to generate rationales and strengthens a model’s prior knowledge using an embedding interpolation technique. This approach allows models to exploit triggers to infer entity boundaries and types instead of solely memorizing the entity words themselves. Through experiments on three well-studied NER datasets, AutoTriggER shows strong label-efficiency, is capable of generalizing to unseen entities, and outperforms the RoBERTa-CRF baseline by nearly 0.5 F1 points on average.
%R 10.18653/v1/2023.eacl-main.219
%U https://aclanthology.org/2023.eacl-main.219
%U https://doi.org/10.18653/v1/2023.eacl-main.219
%P 3011-3025
Markdown (Informal)
[AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction](https://aclanthology.org/2023.eacl-main.219) (Lee et al., EACL 2023)
ACL
- Dong-Ho Lee, Ravi Kiran Selvam, Sheikh Muhammad Sarwar, Bill Yuchen Lin, Fred Morstatter, Jay Pujara, Elizabeth Boschee, James Allan, and Xiang Ren. 2023. AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 3011–3025, Dubrovnik, Croatia. Association for Computational Linguistics.