Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport

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Abstract

Bilingual lexicons form a critical component of various natural language processing applications, including unsupervised and semisupervised machine translation and crosslingual information retrieval. We improve bilingual lexicon induction performance across 40 language pairs with a graph-matching method based on optimal transport. The method is especially strong with low amounts of supervision.

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APA

Marchisio, K., Saad-Eldin, A., Duh, K., Priebe, C., & Koehn, P. (2022). Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022 (pp. 2545–2561). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.emnlp-main.164

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