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        <title>Zero-Shot Cross-lingual Name Retrieval for Low-Resource Languages</title>
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            <title>Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)</title>
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    <abstract>In this paper we address a challenging cross-lingual name retrieval task. Given an English named entity query, we aim to find all name mentions in documents in low-resource languages. We present a novel method which relies on zero annotation or resources from the target language. By leveraging freely available, cross-lingual resources and a small amount of training data from another language, we are able to perform name retrieval on a new language without any additional training data. Our method proceeds in a multi-step process: first, we pre-train a language-independent orthographic encoder using Wikipedia inter-lingual links from dozens of languages. Next, we gather user expectations about important entities in an English comparable document and compare those expected entities with actual spans of the target language text in order to perform name finding. Our method shows 11.6% absolute F-score improvement over state-of-the-art methods.</abstract>
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%0 Conference Proceedings
%T Zero-Shot Cross-lingual Name Retrieval for Low-Resource Languages
%A Blissett, Kevin
%A Ji, Heng
%Y Cherry, Colin
%Y Durrett, Greg
%Y Foster, George
%Y Haffari, Reza
%Y Khadivi, Shahram
%Y Peng, Nanyun
%Y Ren, Xiang
%Y Swayamdipta, Swabha
%S Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F blissett-ji-2019-zero
%X In this paper we address a challenging cross-lingual name retrieval task. Given an English named entity query, we aim to find all name mentions in documents in low-resource languages. We present a novel method which relies on zero annotation or resources from the target language. By leveraging freely available, cross-lingual resources and a small amount of training data from another language, we are able to perform name retrieval on a new language without any additional training data. Our method proceeds in a multi-step process: first, we pre-train a language-independent orthographic encoder using Wikipedia inter-lingual links from dozens of languages. Next, we gather user expectations about important entities in an English comparable document and compare those expected entities with actual spans of the target language text in order to perform name finding. Our method shows 11.6% absolute F-score improvement over state-of-the-art methods.
%R 10.18653/v1/D19-6131
%U https://aclanthology.org/D19-6131
%U https://doi.org/10.18653/v1/D19-6131
%P 275-280

Markdown (Informal)

[Zero-Shot Cross-lingual Name Retrieval for Low-Resource Languages](https://aclanthology.org/D19-6131) (Blissett & Ji, 2019)

• Zero-Shot Cross-lingual Name Retrieval for Low-Resource Languages (Blissett & Ji, 2019)

ACL

• Kevin Blissett and Heng Ji. 2019. Zero-Shot Cross-lingual Name Retrieval for Low-Resource Languages. In Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019), pages 275–280, Hong Kong, China. Association for Computational Linguistics.