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    <titleInfo>
        <title>Improving Candidate Generation for Low-resource Cross-lingual Entity Linking</title>
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    <name type="personal">
        <namePart type="given">Shuyan</namePart>
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    <name type="personal">
        <namePart type="given">Shruti</namePart>
        <namePart type="family">Rijhwani</namePart>
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    <name type="personal">
        <namePart type="given">John</namePart>
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    <name type="personal">
        <namePart type="given">Jaime</namePart>
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    <abstract>Cross-lingual entity linking (XEL) is the task of finding referents in a target-language knowledge base (KB) for mentions extracted from source-language texts. The first step of (X)EL is candidate generation, which retrieves a list of plausible candidate entities from the target-language KB for each mention. Approaches based on resources from Wikipedia have proven successful in the realm of relatively high-resource languages, but these do not extend well to low-resource languages with few, if any, Wikipedia pages. Recently, transfer learning methods have been shown to reduce the demand for resources in the low-resource languages by utilizing resources in closely related languages, but the performance still lags far behind their high-resource counterparts. In this paper, we first assess the problems faced by current entity candidate generation methods for low-resource XEL, then propose three improvements that (1) reduce the disconnect between entity mentions and KB entries, and (2) improve the robustness of the model to low-resource scenarios. The methods are simple, but effective: We experiment with our approach on seven XEL datasets and find that they yield an average gain of 16.9% in Top-30 gold candidate recall, compared with state-of-the-art baselines. Our improved model also yields an average gain of 7.9% in in-KB accuracy of end-to-end XEL.1</abstract>
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    <identifier type="doi">10.1162/tacl_a_00303</identifier>
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        <url>https://aclanthology.org/2020.tacl-1.8</url>
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        <date>2020</date>
        <detail type="volume"><number>8</number></detail>
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            <start>109</start>
            <end>124</end>
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%0 Journal Article
%T Improving Candidate Generation for Low-resource Cross-lingual Entity Linking
%A Zhou, Shuyan
%A Rijhwani, Shruti
%A Wieting, John
%A Carbonell, Jaime
%A Neubig, Graham
%J Transactions of the Association for Computational Linguistics
%D 2020
%V 8
%I MIT Press
%C Cambridge, MA
%F zhou-etal-2020-improving-candidate
%X Cross-lingual entity linking (XEL) is the task of finding referents in a target-language knowledge base (KB) for mentions extracted from source-language texts. The first step of (X)EL is candidate generation, which retrieves a list of plausible candidate entities from the target-language KB for each mention. Approaches based on resources from Wikipedia have proven successful in the realm of relatively high-resource languages, but these do not extend well to low-resource languages with few, if any, Wikipedia pages. Recently, transfer learning methods have been shown to reduce the demand for resources in the low-resource languages by utilizing resources in closely related languages, but the performance still lags far behind their high-resource counterparts. In this paper, we first assess the problems faced by current entity candidate generation methods for low-resource XEL, then propose three improvements that (1) reduce the disconnect between entity mentions and KB entries, and (2) improve the robustness of the model to low-resource scenarios. The methods are simple, but effective: We experiment with our approach on seven XEL datasets and find that they yield an average gain of 16.9% in Top-30 gold candidate recall, compared with state-of-the-art baselines. Our improved model also yields an average gain of 7.9% in in-KB accuracy of end-to-end XEL.1
%R 10.1162/tacl_a_00303
%U https://aclanthology.org/2020.tacl-1.8
%U https://doi.org/10.1162/tacl_a_00303
%P 109-124

Markdown (Informal)

[Improving Candidate Generation for Low-resource Cross-lingual Entity Linking](https://aclanthology.org/2020.tacl-1.8) (Zhou et al., TACL 2020)

• Improving Candidate Generation for Low-resource Cross-lingual Entity Linking (Zhou et al., TACL 2020)

ACL

• Shuyan Zhou, Shruti Rijhwani, John Wieting, Jaime Carbonell, and Graham Neubig. 2020. Improving Candidate Generation for Low-resource Cross-lingual Entity Linking. Transactions of the Association for Computational Linguistics, 8:109–124.