How Do Multilingual Encoders Learn Cross-lingual Representation?

• URL: http://arxiv.org/abs/2207.05737v1
• Date: Tue, 12 Jul 2022 17:57:05 GMT
• Title: How Do Multilingual Encoders Learn Cross-lingual Representation?
• Authors: Shijie Wu
• Abstract summary: Cross-lingual transfer benefits languages with little to no training data by transferring from other languages. This thesis first shows such surprising cross-lingual effectiveness compared against prior art on various tasks. We also look at how to inject different cross-lingual signals into multilingual encoders, and the optimization behavior of cross-lingual transfer with these models.
• Score: 8.409283426564977
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: NLP systems typically require support for more than one language. As different languages have different amounts of supervision, cross-lingual transfer benefits languages with little to no training data by transferring from other languages. From an engineering perspective, multilingual NLP benefits development and maintenance by serving multiple languages with a single system. Both cross-lingual transfer and multilingual NLP rely on cross-lingual representations serving as the foundation. As BERT revolutionized representation learning and NLP, it also revolutionized cross-lingual representations and cross-lingual transfer. Multilingual BERT was released as a replacement for single-language BERT, trained with Wikipedia data in 104 languages. Surprisingly, without any explicit cross-lingual signal, multilingual BERT learns cross-lingual representations in addition to representations for individual languages. This thesis first shows such surprising cross-lingual effectiveness compared against prior art on various tasks. Naturally, it raises a set of questions, most notably how do these multilingual encoders learn cross-lingual representations. In exploring these questions, this thesis will analyze the behavior of multilingual models in a variety of settings on high and low resource languages. We also look at how to inject different cross-lingual signals into multilingual encoders, and the optimization behavior of cross-lingual transfer with these models. Together, they provide a better understanding of multilingual encoders on cross-lingual transfer. Our findings will lead us to suggested improvements to multilingual encoders and cross-lingual transfer.

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