Corpus Augmentation by Sentence Segmentation for Low-Resource Neural Machine Translation
This addresses the issue of limited parallel data for specific language pairs, though it is incremental as it builds on existing augmentation techniques.
The authors tackled the problem of low-resource neural machine translation by augmenting parallel corpora through sentence segmentation and back-translation, resulting in improved translation performance for Japanese-Chinese and Chinese-Japanese pairs on the ASPEC-JC corpus.
Neural Machine Translation (NMT) has been proven to achieve impressive results. The NMT system translation results depend strongly on the size and quality of parallel corpora. Nevertheless, for many language pairs, no rich-resource parallel corpora exist. As described in this paper, we propose a corpus augmentation method by segmenting long sentences in a corpus using back-translation and generating pseudo-parallel sentence pairs. The experiment results of the Japanese-Chinese and Chinese-Japanese translation with Japanese-Chinese scientific paper excerpt corpus (ASPEC-JC) show that the method improves translation performance.