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• W3200726972 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." @default.
• W3200726972 created "2021-09-27" @default.
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• W3200726972 date "2021-01-01" @default.
• W3200726972 modified "2023-09-27" @default.
• W3200726972 title "A Simple and Effective Method To Eliminate the Self Language Bias in Multilingual Representations" @default.
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• W3200726972 doi "https://doi.org/10.18653/v1/2021.emnlp-main.470" @default.
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