Impact of Cross-Lingual Pre-training Language Scale on Unicoder Accuracy for Natural Language Inference

Abstract

Abstract: We present Unicoder, a universal language encoder that is insensitive to different languages. Given an arbitrary NLP task, a model can be trained with Unicoder using training data in one language and directly applied to inputs of the same task in other languages. Comparing to similar efforts such as Multilingual BERT and XLM, three new cross-lingual pre-training tasks are proposed, including cross-lingual word recovery, cross-lingual paraphrase classification and cross-lingual masked language model. These tasks help Unicoder learn the mappings among different languages from more perspectives.

Research Question

What is the impact of scaling the number of languages in cross-lingual pre-training tasks (e.g., 5 vs. 20 languages) on the accuracy of Unicoder for cross-lingual natural language inference tasks?

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