How Lexical is Bilingual Lexicon Induction?
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Abstract (arXiv)
In contemporary machine learning approaches to bilingual lexicon induction (BLI), a model learns a mapping between the embedding spaces of a language pair. Recently, retrieve-and-rank approach to BLI has achieved state of the art results on the task. However, the problem remains challenging in low-resource settings, due to the paucity of data. The task is complicated by factors such as lexical variation across languages. We argue that the incorporation of additional lexical information into the recent retrieve-and-rank approach should improve lexicon induction. We demonstrate the efficacy of our proposed approach on XLING, improving over the previous state of the art by an average of 2% across all language pairs.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 34 references that are in the map. First posted on arXiv 5 Apr 2024.
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References · 19
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7 more
- 2020LNMap: Departures from Isomorphic Assumption in Bilingual Lexicon Induction Through Non-Linear Mapping in Latent Spacecited by 23
- 2020Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentencescited by 19
- 2021Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignmentcited by 19
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- 2022Improving Word Translation via Two-Stage Contrastive Learningcited by 18
- 2019Interactive Refinement of Cross-Lingual Word Embeddingscited by 13
- 2022Improving Bilingual Lexicon Induction with Cross-Encoder Rerankingcited by 7
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