mlsanityData to 7 Oct 2026

Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction

arXiv 2106.03084 ↗First posted 6 Jun 2021cs.CLcited by 12

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Abstract (arXiv)

Bilingual Lexicon Induction (BLI) aims to map words in one language to their translations in another, and is typically through learning linear projections to align monolingual word representation spaces. Two classes of word representations have been explored for BLI: static word embeddings and contextual representations, but there is no studies to combine both. In this paper, we propose a simple yet effective mechanism to combine the static word embeddings and the contextual representations to utilize the advantages of both paradigms. We test the combination mechanism on various language pairs under the supervised and unsupervised BLI benchmark settings. Experiments show that our mechanism consistently improves performances over robust BLI baselines on all language pairs by averagely improving 3.2 points in the supervised setting, and 3.1 points in the unsupervised setting.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 23 of its 43 references that are in the map. First posted on arXiv 6 Jun 2021.

Builds on

23 of 43 references.

References · 23

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11 more
  1. 2019Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsingcited by 69
  2. 2018On the Limitations of Unsupervised Bilingual Dictionary Inductioncited by 61
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  4. 2019Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spacescited by 35
  5. 2019Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Frameworkcited by 26
  6. 2020Cross-lingual Alignment Methods for Multilingual BERT: A Comparative Studycited by 24
  7. 2020LNMap: Departures from Isomorphic Assumption in Bilingual Lexicon Induction Through Non-Linear Mapping in Latent Spacecited by 23
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  11. 2019Revisiting Adversarial Autoencoder for Unsupervised Word Translation with Cycle Consistency and Improved Trainingcited by 12

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Citing papers per year: 2022: 3; 2023: 4; 2024: 3; 2025: 2.

12 papers.