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MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages ...
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Improving Candidate Generation for Low-resource Cross-lingual Entity Linking
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In: Transactions of the Association for Computational Linguistics, Vol 8, Pp 109-124 (2020) (2020)
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Towards Zero-resource Cross-lingual Entity Linking ...
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Abstract:
Cross-lingual entity linking (XEL) grounds named entities in a source language to an English Knowledge Base (KB), such as Wikipedia. XEL is challenging for most languages because of limited availability of requisite resources. However, much previous work on XEL has been on simulated settings that actually use significant resources (e.g. source language Wikipedia, bilingual entity maps, multilingual embeddings) that are unavailable in truly low-resource languages. In this work, we first examine the effect of these resource assumptions and quantify how much the availability of these resource affects overall quality of existing XEL systems. Next, we propose three improvements to both entity candidate generation and disambiguation that make better use of the limited data we do have in resource-scarce scenarios. With experiments on four extremely low-resource languages, we show that our model results in gains of 6-23% in end-to-end linking accuracy. ... : Accepted by EMNLP DeepLo workshop 2019 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1909.13180
https://dx.doi.org/10.48550/arxiv.1909.13180
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