Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 39 of its 59 references that are in the map. First posted on arXiv 25 Oct 2022.
Builds on
39 of 59 references.
References · 39
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27 more
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Built on by
Citing papers per year: 2023: 3; 2024: 2; 2025: 1.
6 papers.
- 2024The Zeno's Paradox of `Low-Resource' Languagescited by 12
- 2023Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsingcited by 2
- 2023ProMap: Effective Bilingual Lexicon Induction via Language Model Promptingcited by 2
- 2023Accessing Higher Dimensions for Unsupervised Word Translationcited by 0
- 2024A survey of neural-network-based methods utilising comparable data for finding translation equivalentscited by 0
- 2025Asymptotically perfect seeded graph matching without edge correlation (and applications to inference)cited by 0
- 2025Asymptotically perfect seeded graph matching without edge correlation (and applications to inference)3 Jun 2025
- 2024The Zeno's Paradox of `Low-Resource' Languages28 Oct 2024
- 2024A survey of neural-network-based methods utilising comparable data for finding translation equivalents19 Oct 2024
- 2023ProMap: Effective Bilingual Lexicon Induction via Language Model Prompting28 Oct 2023
- 2023Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing9 Jul 2023
- 2023Accessing Higher Dimensions for Unsupervised Word Translation23 May 2023