Alignment Enhancement in Multilingual Models via Intermediate-Task Training

Abstract

Abstract: State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models. These models are generally trained on data in a single language (usually English), and cannot be directly used beyond that language. Since collecting data in every language is not realistic, there has been a growing interest in cross-lingual language understanding (XLU) and low-resource cross-language transfer. In this work, we construct an evaluation set for XLU by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNL

Research Question

What is the impact of intermediate-task training on the alignment of multilingual language models, as measured by the Cross-Lingual Natural Language Inference (XNLI) benchmark?

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