On the ability of monolingual models to learn language-agnostic representations

• URL: http://arxiv.org/abs/2109.01942v1
• Date: Sat, 4 Sep 2021 22:09:44 GMT
• Title: On the ability of monolingual models to learn language-agnostic representations
• Authors: Leandro Rodrigues de Souza, Rodrigo Nogueira, Roberto Lotufo
• Abstract summary: We show that monolingual models pretrained and finetuned on different languages achieve competitive performance. For example, models pretrained on distant languages such as German and Portuguese perform similarly on English tasks.
• Score: 2.604227467422371
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pretrained multilingual models have become a de facto default approach for zero-shot cross-lingual transfer. Previous work has shown that these models are able to achieve cross-lingual representations when pretrained on two or more languages with shared parameters. In this work, we provide evidence that a model can achieve language-agnostic representations even when pretrained on a single language. That is, we find that monolingual models pretrained and finetuned on different languages achieve competitive performance compared to the ones that use the same target language. Surprisingly, the models show a similar performance on a same task regardless of the pretraining language. For example, models pretrained on distant languages such as German and Portuguese perform similarly on English tasks.

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