MEXMA: Token-level objectives improve sentence representations

João Maria Janeiro, Benjamin Piwowarski, Patrick Gallinari, Loic Barrault

Keywords: Natural Language Processing, Cross lingual sentence encoders, Language alignment, Translations, Representation Learning, Translations

TL;DR: We present a novel sota method for creating aligned sentence representations across languages, that also performs better on downstream tasks, by combining both token level and sentence level objectives during training.

Abstract: Cross-lingual sentence encoders (CLSE) create fixed-size sentence representations with aligned translations. Current pre-trained CLSE approaches use sentence-level objectives only. This can lead to loss of information, especially for tokens, which then degrades the sentence representation. We propose MEXMA, a novel approach that integrates both sentence-level and token-level objectives. The sentence representation in one language is used to predict masked tokens in another language, with both the sentence representation and $\textit{all tokens directly updating the encoder}$. We show that adding token-level objectives greatly improves the sentence representation quality across several tasks. Our approach outperforms current pre-trained cross-lingual sentence encoders on bitext mining as well as several downstream tasks. We also analyse the information encoded in our tokens, and how the sentence representation is built from them.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 2199