AI Seminar sponsored by Apple

Junjie Hu -- Cross-Lingual and Cross Domain Transfer for Neural Machine Translation

Abstract: Neural machine translation (NMT) models have achieved state-of-the-art performance on many popular benchmark datasets for high-resourced languages (HRL). However, training such deep networks requires a large amount of parallel sentences, and the translation performance in low-resourced languages (LRL) falls far behind that of high-resourced languages. This problem is further exacerbated by domain mismatch, which is unavoidable on many collected datasets. To remedy this problem, we apply transfer learning to adapting NMT models across languages and across domains. In the first part of the talk, we introduce a method of rapidly adapting NMT models to new languages by continuously training on data related to the LRL. In the second part, we propose to adapt NMT models to new domains by generating synthetic data that are closely related to in-domain data.