Back-translated Data Research Articles

Making the most of comparable corpora in Neural Machine Translation: a case study

Comparable corpora can benefit the development of Neural Machine Translation models, in particular for under-resourced languages. We present a case study centred on the exploitation of a large comparable corpus for Basque-Spanish, created from independently-produced news by the Basque public broadcaster eitb, where we evaluate the impact of different techniques to exploit the original data, in order to complement parallel datasets for this language pair in both translation directions. Two efficient methods for parallel sentence mining are explored, which identified a common core of approximately half of the total number of aligned sentences, each one uniquely identifying valid parallel sentences not captured by the other method. Filtering the data via identification of length-difference outliers proved highly effective to improve the models, as was the use of tags to discriminate between comparable and parallel data in the training corpora. The use of backtranslated data is also evaluated in this work, with results indicating that alignment-based datasets remain the most beneficial, although complementary backtranslations should also be included to fully exploit the available comparable data. Overall, the results in this work demonstrate that this type of data needs to be carefully analysed prior to its use as training data for Neural Machine Translation, since issues such as information imbalance between source and target data can lead to unoptimal results for a given translation pair.

Tagged Back-translation Revisited: Why Does It Really Work?

In this paper, we show that neural machine translation (NMT) systems trained on large back-translated data overfit some of the characteristics of machine-translated texts. Such NMT systems better translate human-produced translations, i.e., translationese, but may largely worsen the translation quality of original texts. Our analysis reveals that adding a simple tag to back-translations prevents this quality degradation and improves on average the overall translation quality by helping the NMT system to distinguish back-translated data from original parallel data during training. We also show that, in contrast to high-resource configurations, NMT systems trained in low-resource settings are much less vulnerable to overfit back-translations. We conclude that the back-translations in the training data should always be tagged especially when the origin of the text to be translated is unknown.

Revisiting Back-Translation for Low-Resource Machine Translation Between Chinese and Vietnamese

Back-translation (BT) has been widely used and become one of standard techniques for data augmentation in Neural Machine Translation (NMT), BT has proven to be beneficial for improving the performance of translation effectively, especially for low-resource scenarios. While most previous works related to BT mainly focus on European languages with high relatedness, few of them study less-related languages in other areas around the world. In this paper, we choose the language pair with less relatedness in Asia: Chinese and Vietnamese, to investigate the impacts of BT on extremely low-resource machine translation between them. We first discuss the similarities and differences between the two languages, then evaluate and compare the effects of different sizes of back-translated data on NMT and Statistical Machine Translation (SMT) models for Chinese-Vietnamese and Vietnamese-Chinese, with both character-based and word-based settings, and conduct further analysis on the translation outputs from several aspects. Some conclusions from previous works are partially confirmed and we also draw some new findings and conclusions, which are beneficial to understand BT further and deeper for translation between less-related low-resource languages.