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        <title>Zero-shot Entity Linking with Efficient Long Range Sequence Modeling</title>
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        <namePart type="given">Zonghai</namePart>
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    <abstract>This paper considers the problem of zero-shot entity linking, in which a link in the test time may not present in training. Following the prevailing BERT-based research efforts, we find a simple yet effective way is to expand the long-range sequence modeling. Unlike many previous methods, our method does not require expensive pre-training of BERT with long position embeddings. Instead, we propose an efficient position embeddings initialization method called Embedding-repeat, which initializes larger position embeddings based on BERT-Base. On the zero-shot entity linking dataset, our method improves the STOA from 76.06% to 79.08%, and for its long data, the corresponding improvement is from 74.57% to 82.14%. Our experiments suggest the effectiveness of long-range sequence modeling without retraining the BERT model.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.228</identifier>
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        <url>https://aclanthology.org/2020.findings-emnlp.228</url>
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        <date>2020-11</date>
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%0 Conference Proceedings
%T Zero-shot Entity Linking with Efficient Long Range Sequence Modeling
%A Yao, Zonghai
%A Cao, Liangliang
%A Pan, Huapu
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F yao-etal-2020-zero
%X This paper considers the problem of zero-shot entity linking, in which a link in the test time may not present in training. Following the prevailing BERT-based research efforts, we find a simple yet effective way is to expand the long-range sequence modeling. Unlike many previous methods, our method does not require expensive pre-training of BERT with long position embeddings. Instead, we propose an efficient position embeddings initialization method called Embedding-repeat, which initializes larger position embeddings based on BERT-Base. On the zero-shot entity linking dataset, our method improves the STOA from 76.06% to 79.08%, and for its long data, the corresponding improvement is from 74.57% to 82.14%. Our experiments suggest the effectiveness of long-range sequence modeling without retraining the BERT model.
%R 10.18653/v1/2020.findings-emnlp.228
%U https://aclanthology.org/2020.findings-emnlp.228
%U https://doi.org/10.18653/v1/2020.findings-emnlp.228
%P 2517-2522

Markdown (Informal)

[Zero-shot Entity Linking with Efficient Long Range Sequence Modeling](https://aclanthology.org/2020.findings-emnlp.228) (Yao et al., Findings 2020)

• Zero-shot Entity Linking with Efficient Long Range Sequence Modeling (Yao et al., Findings 2020)

ACL

• Zonghai Yao, Liangliang Cao, and Huapu Pan. 2020. Zero-shot Entity Linking with Efficient Long Range Sequence Modeling. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2517–2522, Online. Association for Computational Linguistics.