Exploring Cross-Lingual Transfer Learning with Unsupervised Machine Translation

Chao Wang

Abstract: In Natural Language Understanding (NLU), to facilitate Cross-Lingual Transfer Learning (CLTL), especially CLTL between distant languages, we integrate CLTL with Machine Translation (MT), and thereby propose a novel CLTL model named Translation Aided Language Learner (TALL). TALL is constructed as a standard transformer, where the encoder is a pre-trained multilingual language model. The training of TALL includes an MT-oriented pre-training and an NLU-oriented fine-tuning. To make use of unannotated data, we implement the recently proposed Unsupervised Machine Translation (UMT) technique in the MT-oriented pre-training of TALL. The experimental results show that the application of UMT enables TALL to consistently achieve better CLTL performance than our baseline model, which is the pre-trained multilingual language model serving as the encoder of TALL, without using more annotated data, and the performance gain is relatively prominent in the case of distant languages.