Papers with Multilingual representations

2 papers
Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer (2020.acl-main)

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Challenge: Multilingual word embeddings embed words from many languages into a single semantic space such that words with similar meanings are close to each other regardless of the language.
Approach: They propose to use multilingual word embeddings to align embeddable words from multiple languages into a single semantic space so that words with similar meanings are close to each other regardless of the language.
Outcome: The proposed model can be used to learn gender bias in multilingual representations and to improve transfer learning.
Iterative Multilingual Spectral Attribute Erasure (2025.emnlp-main)

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Challenge: Existing methods for debiasing are unable to exploit this opportunity because they operate on individual languages.
Approach: They propose to iterate multilingual spectral attribute error (IMSAE) to mitigate joint bias subspaces across multiple languages through iterative SVD-based truncation.
Outcome: The proposed method outperforms monolingual and cross-lingual approaches while maintaining model utility.

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