Multilingual Transformer Encoders: a Word-Level Task-Agnostic Evaluation

• URL: http://arxiv.org/abs/2207.09076v1
• Date: Tue, 19 Jul 2022 05:23:18 GMT
• Title: Multilingual Transformer Encoders: a Word-Level Task-Agnostic Evaluation
• Authors: F\'elix Gaschi, Fran\c{c}ois Plesse, Parisa Rastin and Yannick Toussaint
• Abstract summary: Some Transformer-based models can perform cross-lingual transfer learning. We propose a word-level task-agnostic method to evaluate the alignment of contextualized representations built by such models.
• Score: 0.6882042556551609
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Some Transformer-based models can perform cross-lingual transfer learning: those models can be trained on a specific task in one language and give relatively good results on the same task in another language, despite having been pre-trained on monolingual tasks only. But, there is no consensus yet on whether those transformer-based models learn universal patterns across languages. We propose a word-level task-agnostic method to evaluate the alignment of contextualized representations built by such models. We show that our method provides more accurate translated word pairs than previous methods to evaluate word-level alignment. And our results show that some inner layers of multilingual Transformer-based models outperform other explicitly aligned representations, and even more so according to a stricter definition of multilingual alignment.

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