A Simple and Effective Method To Eliminate the Self Language Bias in Multilingual Representations

• URL: http://arxiv.org/abs/2109.04727v1
• Date: Fri, 10 Sep 2021 08:15:37 GMT
• Title: A Simple and Effective Method To Eliminate the Self Language Bias in Multilingual Representations
• Authors: Ziyi Yang, Yinfei Yang, Daniel Cer and Eric Darve
• Abstract summary: Language agnostic and semantic-language information isolation is an emerging research direction for multilingual representations models. "Language Information Removal (LIR)" factors out language identity information from semantic related components in multilingual representations pre-trained on multi-monolingual data. LIR reveals that for weak-alignment multilingual systems, the principal components of semantic spaces primarily encodes language identity information.
• Score: 7.571549274473274
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Language agnostic and semantic-language information isolation is an emerging research direction for multilingual representations models. We explore this problem from a novel angle of geometric algebra and semantic space. A simple but highly effective method "Language Information Removal (LIR)" factors out language identity information from semantic related components in multilingual representations pre-trained on multi-monolingual data. A post-training and model-agnostic method, LIR only uses simple linear operations, e.g. matrix factorization and orthogonal projection. LIR reveals that for weak-alignment multilingual systems, the principal components of semantic spaces primarily encodes language identity information. We first evaluate the LIR on a cross-lingual question answer retrieval task (LAReQA), which requires the strong alignment for the multilingual embedding space. Experiment shows that LIR is highly effectively on this task, yielding almost 100% relative improvement in MAP for weak-alignment models. We then evaluate the LIR on Amazon Reviews and XEVAL dataset, with the observation that removing language information is able to improve the cross-lingual transfer performance.

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