@inproceedings{zhang-etal-2023-2iner,
title = "2{INER}: Instructive and In-Context Learning on Few-Shot Named Entity Recognition",
author = "Zhang, Jiasheng and
Liu, Xikai and
Lai, Xinyi and
Gao, Yan and
Wang, Shusen and
Hu, Yao and
Lin, Yiqing",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.259",
doi = "10.18653/v1/2023.findings-emnlp.259",
pages = "3940--3951",
abstract = "Prompt-based learning has emerged as a powerful technique in natural language processing (NLP) due to its ability to leverage pre-training knowledge for downstream few-shot tasks. In this paper, we propose 2INER, a novel text-to-text framework for Few-Shot Named Entity Recognition (NER) tasks. Our approach employs instruction finetuning based on InstructionNER to enable the model to effectively comprehend and process task-specific instructions, including both main and auxiliary tasks. We also introduce a new auxiliary task, called Type Extracting, to enhance the model{'}s understanding of entity types in the overall semantic context of a sentence. To facilitate in-context learning, we concatenate examples to the input, enabling the model to learn from additional contextual information. Experimental results on four datasets demonstrate that our approach outperforms existing Few-Shot NER methods and remains competitive with state-of-the-art standard NER algorithms.",
}
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<abstract>Prompt-based learning has emerged as a powerful technique in natural language processing (NLP) due to its ability to leverage pre-training knowledge for downstream few-shot tasks. In this paper, we propose 2INER, a novel text-to-text framework for Few-Shot Named Entity Recognition (NER) tasks. Our approach employs instruction finetuning based on InstructionNER to enable the model to effectively comprehend and process task-specific instructions, including both main and auxiliary tasks. We also introduce a new auxiliary task, called Type Extracting, to enhance the model’s understanding of entity types in the overall semantic context of a sentence. To facilitate in-context learning, we concatenate examples to the input, enabling the model to learn from additional contextual information. Experimental results on four datasets demonstrate that our approach outperforms existing Few-Shot NER methods and remains competitive with state-of-the-art standard NER algorithms.</abstract>
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%0 Conference Proceedings
%T 2INER: Instructive and In-Context Learning on Few-Shot Named Entity Recognition
%A Zhang, Jiasheng
%A Liu, Xikai
%A Lai, Xinyi
%A Gao, Yan
%A Wang, Shusen
%A Hu, Yao
%A Lin, Yiqing
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zhang-etal-2023-2iner
%X Prompt-based learning has emerged as a powerful technique in natural language processing (NLP) due to its ability to leverage pre-training knowledge for downstream few-shot tasks. In this paper, we propose 2INER, a novel text-to-text framework for Few-Shot Named Entity Recognition (NER) tasks. Our approach employs instruction finetuning based on InstructionNER to enable the model to effectively comprehend and process task-specific instructions, including both main and auxiliary tasks. We also introduce a new auxiliary task, called Type Extracting, to enhance the model’s understanding of entity types in the overall semantic context of a sentence. To facilitate in-context learning, we concatenate examples to the input, enabling the model to learn from additional contextual information. Experimental results on four datasets demonstrate that our approach outperforms existing Few-Shot NER methods and remains competitive with state-of-the-art standard NER algorithms.
%R 10.18653/v1/2023.findings-emnlp.259
%U https://aclanthology.org/2023.findings-emnlp.259
%U https://doi.org/10.18653/v1/2023.findings-emnlp.259
%P 3940-3951
Markdown (Informal)
[2INER: Instructive and In-Context Learning on Few-Shot Named Entity Recognition](https://aclanthology.org/2023.findings-emnlp.259) (Zhang et al., Findings 2023)
ACL