On the Language Neutrality of Pre-trained Multilingual Representations

• URL: http://arxiv.org/abs/2004.05160v4
• Date: Tue, 29 Sep 2020 18:48:19 GMT
• Title: On the Language Neutrality of Pre-trained Multilingual Representations
• Authors: Jind\v{r}ich Libovick\'y, Rudolf Rosa, Alexander Fraser
• Abstract summary: We investigate the language-neutrality of multilingual contextual embeddings directly and with respect to lexical semantics. Our results show that contextual embeddings are more language-neutral and, in general, more informative than aligned static word-type embeddings. We show how to reach state-of-the-art accuracy on language identification and match the performance of statistical methods for word alignment of parallel sentences.
• Score: 70.93503607755055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multilingual contextual embeddings, such as multilingual BERT and XLM-RoBERTa, have proved useful for many multi-lingual tasks. Previous work probed the cross-linguality of the representations indirectly using zero-shot transfer learning on morphological and syntactic tasks. We instead investigate the language-neutrality of multilingual contextual embeddings directly and with respect to lexical semantics. Our results show that contextual embeddings are more language-neutral and, in general, more informative than aligned static word-type embeddings, which are explicitly trained for language neutrality. Contextual embeddings are still only moderately language-neutral by default, so we propose two simple methods for achieving stronger language neutrality: first, by unsupervised centering of the representation for each language and second, by fitting an explicit projection on small parallel data. Besides, we show how to reach state-of-the-art accuracy on language identification and match the performance of statistical methods for word alignment of parallel sentences without using parallel data.

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