Inducing Language-Agnostic Multilingual Representations
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Inducing Language-Agnostic Multilingual Representations. / Zhao, Wei; Eger, Steffan; Bjerva, Johannes; Augenstein, Isabelle.
In: arXiv.org, Vol. CoRR 2020, 2020.Research output: Contribution to journal › Journal article › Research
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TY - JOUR
T1 - Inducing Language-Agnostic Multilingual Representations
AU - Zhao, Wei
AU - Eger, Steffan
AU - Bjerva, Johannes
AU - Augenstein, Isabelle
PY - 2020
Y1 - 2020
N2 - Multilingual representations have the potential to make cross-lingual systems available to the vast majority of languages in the world. However, they currently require large pretraining corpora, or assume access to typologically similar languages. In this work, we address these obstacles by removing language identity signals from multilingual embeddings. We examine three approaches for this: 1) re-aligning the vector spaces of target languages (all together) to a pivot source language; 2) removing languages-specific means and variances, which yields better discriminativeness of embeddings as a by-product; and 3) normalizing input texts by removing morphological contractions and sentence reordering, thus yielding language-agnostic representations. We evaluate on the tasks of XNLI and reference-free MT evaluation of varying difficulty across 19 selected languages. Our experiments demonstrate the language-agnostic behavior of our multilingual representations, which manifest the potential of zero-shot cross-lingual transfer to distant and low-resource languages, and decrease the performance gap by 8.9 points (M-BERT) and 18.2 points (XLM-R) on average across all tasks and languages. We make our codes and models available.
AB - Multilingual representations have the potential to make cross-lingual systems available to the vast majority of languages in the world. However, they currently require large pretraining corpora, or assume access to typologically similar languages. In this work, we address these obstacles by removing language identity signals from multilingual embeddings. We examine three approaches for this: 1) re-aligning the vector spaces of target languages (all together) to a pivot source language; 2) removing languages-specific means and variances, which yields better discriminativeness of embeddings as a by-product; and 3) normalizing input texts by removing morphological contractions and sentence reordering, thus yielding language-agnostic representations. We evaluate on the tasks of XNLI and reference-free MT evaluation of varying difficulty across 19 selected languages. Our experiments demonstrate the language-agnostic behavior of our multilingual representations, which manifest the potential of zero-shot cross-lingual transfer to distant and low-resource languages, and decrease the performance gap by 8.9 points (M-BERT) and 18.2 points (XLM-R) on average across all tasks and languages. We make our codes and models available.
M3 - Journal article
VL - CoRR 2020
JO - arXiv.org
JF - arXiv.org
ER -
ID: 254998886