Neural Machine Translation with Diversity-Enabled Translation Memory

Quang Chieu Nguyen, Xuan-Dung Doan, Van-Vinh Nguyen, Khac-Hoai Nam Bui

Abstract: Neural machine translation (NMT) using translation memory (TM) has been introduced as an emergent technique for improving machine translation systems (MTS). In this study, we propose an end-to-end NMT model with TM by exploiting the diversity of the retrieval-augmented phase using maximal marginal relevance (MMR). In particular, the proposed model is designed with monolingual TM, which is able to support low-resource scenarios. Furthermore, the memory retriever and translation models are jointly trained to improve translation performance. For the experiment, we use IWSLT15 (En \(\longleftrightarrow \) Vi) as a benchmark dataset to evaluate the performance of the proposed method. Accordingly, the experiential results show the effectiveness of the proposed method compared with strong baselines in this research field.