Spanish-Turkish Low-Resource Machine Translation: Unsupervised Learning vs Round-Tripping

Abstract

The quality of data-driven Machine Translation (MT) strongly depends on the quantity as well as the quality of the training dataset. However, collecting a large set of training parallel texts is not easy in practice. Although various approaches have already been proposed to overcome this issue, the lack of large parallel corpora still poses a major practical problem for many language pairs. Since monolingual data plays an important role in boosting fluency for Neural MT (NMT) models, this paper investigates and compares the performance of two learning-based translation approaches for Spanish-Turkish translation as a low-resource setting in case we only have access to large sets of monolingual data in each language; 1) Unsupervised Learning approach, and 2) Round-Tripping approach. Either approach completely removes the need for bilingual data or enables us to train the NMT system relying on monolingual data only. We utilize an Attention-based NMT (Attentional NMT) model, which leverages a careful initialization of the parameters, the denoising effect of language models, and the automatic generation of bilingual data. Our experimental results demonstrate that the Unsupervised Learning approach outperforms the Round-Tripping approach in Spanish-Turkish translation and vice versa. These results confirm that the Unsupervised Learning approach is still a reliable learning-based translation technique for Spanish-Turkish low-resource NMT.