Multi-Source Neural Machine Translation with Data Augmentation
This work addresses data scarcity in multi-source translation, but it is incremental as it builds on existing NMT methods.
The paper tackles the problem of incomplete multi-source parallel corpora for neural machine translation by proposing a data augmentation approach, achieving significant gains when source and target languages are similar.
Multi-source translation systems translate from multiple languages to a single target language. By using information from these multiple sources, these systems achieve large gains in accuracy. To train these systems, it is necessary to have corpora with parallel text in multiple sources and the target language. However, these corpora are rarely complete in practice due to the difficulty of providing human translations in all of the relevant languages. In this paper, we propose a data augmentation approach to fill such incomplete parts using multi-source neural machine translation (NMT). In our experiments, results varied over different language combinations but significant gains were observed when using a source language similar to the target language.