Towards Guided Back-translation for Low-resource languages- A Case Study on Kabyle-French

This paper explores the development of a first Neural Machine Translation for a low-resource language pair, Kabyle- French. Contrary to the idea that more sentences would enhance the translator's quality, our study suggests that using Back-translation on low-quality monolingual corpora can harm the translation performance. We deepen an analysis of the translator's performance to identify the optimal operational range, allowing us to use the Back-translation effectively and thus reduce the error rate during the generation of the parallel corpus. Our results highlight the central role played by the quality of the monolingual corpus in determining the effectiveness of Back-translation, highlighting the benefits of prioritizing data quality over quantity. Furthermore, we introduce a first method to evaluate the effectiveness of Back-translation, taking into account sentence length and part-of-speech tags. This evaluation demonstrates an improved performance when applying Back-translation to sentences of medium length.