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        <title>Improving Few-Shot Domain Transfer for Named Entity Disambiguation with Pattern Exploitation</title>
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            <namePart type="given">Yoav</namePart>
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        <name type="personal">
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    <abstract>Named entity disambiguation (NED) is a critical subtask of entity linking, which seeks to connect knowledge base entities with textual mentions of those entities. Naturally, the performance of a model depends on the domain it was trained on; thus, reducing the amount of data required to train models is advantageous. In this work, we leverage recent research on pattern exploitation for NED and explore whether it can reduce the amount of data required for domain adaptation by reformulating the disambiguation task as a masked language modeling problem. Using ADAPET (Tam et al., 2021), which implements a new approach for few-shot learning using fine-tuned transformer-based language models, we produce an NED model which yields, without any sacrifice of in-domain accuracy, a 7% improvement in zero-shot cross-domain performance as evaluated on NEDMed, a new NED dataset of mental health news which we release with this work.</abstract>
    <identifier type="citekey">blair-bar-2022-improving</identifier>
    <identifier type="doi">10.18653/v1/2022.findings-emnlp.506</identifier>
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%0 Conference Proceedings
%T Improving Few-Shot Domain Transfer for Named Entity Disambiguation with Pattern Exploitation
%A Blair, Philip
%A Bar, Kfir
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F blair-bar-2022-improving
%X Named entity disambiguation (NED) is a critical subtask of entity linking, which seeks to connect knowledge base entities with textual mentions of those entities. Naturally, the performance of a model depends on the domain it was trained on; thus, reducing the amount of data required to train models is advantageous. In this work, we leverage recent research on pattern exploitation for NED and explore whether it can reduce the amount of data required for domain adaptation by reformulating the disambiguation task as a masked language modeling problem. Using ADAPET (Tam et al., 2021), which implements a new approach for few-shot learning using fine-tuned transformer-based language models, we produce an NED model which yields, without any sacrifice of in-domain accuracy, a 7% improvement in zero-shot cross-domain performance as evaluated on NEDMed, a new NED dataset of mental health news which we release with this work.
%R 10.18653/v1/2022.findings-emnlp.506
%U https://aclanthology.org/2022.findings-emnlp.506
%U https://doi.org/10.18653/v1/2022.findings-emnlp.506
%P 6797-6810

Markdown (Informal)

[Improving Few-Shot Domain Transfer for Named Entity Disambiguation with Pattern Exploitation](https://aclanthology.org/2022.findings-emnlp.506) (Blair & Bar, Findings 2022)

• Improving Few-Shot Domain Transfer for Named Entity Disambiguation with Pattern Exploitation (Blair & Bar, Findings 2022)

ACL

• Philip Blair and Kfir Bar. 2022. Improving Few-Shot Domain Transfer for Named Entity Disambiguation with Pattern Exploitation. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6797–6810, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.