@inproceedings{lee-etal-2022-good,
title = "Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource {NER}",
author = "Lee, Dong-Ho and
Kadakia, Akshen and
Tan, Kangmin and
Agarwal, Mahak and
Feng, Xinyu and
Shibuya, Takashi and
Mitani, Ryosuke and
Sekiya, Toshiyuki and
Pujara, Jay and
Ren, Xiang",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.192",
doi = "10.18653/v1/2022.acl-long.192",
pages = "2687--2700",
abstract = "Recent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates. Similar attempts have been made on named entity recognition (NER) which manually design templates to predict entity types for every text span in a sentence. However, such methods may suffer from error propagation induced by entity span detection, high cost due to enumeration of all possible text spans, and omission of inter-dependencies among token labels in a sentence. Here we present a simple demonstration-based learning method for NER, which lets the input be prefaced by task demonstrations for in-context learning. We perform a systematic study on demonstration strategy regarding what to include (entity examples, with or without surrounding context), how to select the examples, and what templates to use. Results on in-domain learning and domain adaptation show that the model{'}s performance in low-resource settings can be largely improved with a suitable demonstration strategy (e.g., a 4-17{\%} improvement on 25 train instances). We also find that good demonstration can save many labeled examples and consistency in demonstration contributes to better performance.",
}
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<abstract>Recent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates. Similar attempts have been made on named entity recognition (NER) which manually design templates to predict entity types for every text span in a sentence. However, such methods may suffer from error propagation induced by entity span detection, high cost due to enumeration of all possible text spans, and omission of inter-dependencies among token labels in a sentence. Here we present a simple demonstration-based learning method for NER, which lets the input be prefaced by task demonstrations for in-context learning. We perform a systematic study on demonstration strategy regarding what to include (entity examples, with or without surrounding context), how to select the examples, and what templates to use. Results on in-domain learning and domain adaptation show that the model’s performance in low-resource settings can be largely improved with a suitable demonstration strategy (e.g., a 4-17% improvement on 25 train instances). We also find that good demonstration can save many labeled examples and consistency in demonstration contributes to better performance.</abstract>
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%0 Conference Proceedings
%T Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER
%A Lee, Dong-Ho
%A Kadakia, Akshen
%A Tan, Kangmin
%A Agarwal, Mahak
%A Feng, Xinyu
%A Shibuya, Takashi
%A Mitani, Ryosuke
%A Sekiya, Toshiyuki
%A Pujara, Jay
%A Ren, Xiang
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F lee-etal-2022-good
%X Recent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates. Similar attempts have been made on named entity recognition (NER) which manually design templates to predict entity types for every text span in a sentence. However, such methods may suffer from error propagation induced by entity span detection, high cost due to enumeration of all possible text spans, and omission of inter-dependencies among token labels in a sentence. Here we present a simple demonstration-based learning method for NER, which lets the input be prefaced by task demonstrations for in-context learning. We perform a systematic study on demonstration strategy regarding what to include (entity examples, with or without surrounding context), how to select the examples, and what templates to use. Results on in-domain learning and domain adaptation show that the model’s performance in low-resource settings can be largely improved with a suitable demonstration strategy (e.g., a 4-17% improvement on 25 train instances). We also find that good demonstration can save many labeled examples and consistency in demonstration contributes to better performance.
%R 10.18653/v1/2022.acl-long.192
%U https://aclanthology.org/2022.acl-long.192
%U https://doi.org/10.18653/v1/2022.acl-long.192
%P 2687-2700
Markdown (Informal)
[Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER](https://aclanthology.org/2022.acl-long.192) (Lee et al., ACL 2022)
ACL
- Dong-Ho Lee, Akshen Kadakia, Kangmin Tan, Mahak Agarwal, Xinyu Feng, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, and Xiang Ren. 2022. Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2687–2700, Dublin, Ireland. Association for Computational Linguistics.