Towards Machine Translation for the Kurdish Language
This work addresses the lack of machine translation resources for Kurdish speakers, but it is incremental as it builds on existing neural methods for a new language.
The paper tackles the problem of machine translation for the less-resourced Kurdish language, specifically the Sorani dialect, by addressing issues like scarce parallel data and text processing challenges, resulting in demonstrated improvements in translation performance through tasks such as tokenization.
Machine translation is the task of translating texts from one language to another using computers. It has been one of the major tasks in natural language processing and computational linguistics and has been motivating to facilitate human communication. Kurdish, an Indo-European language, has received little attention in this realm due to the language being less-resourced. Therefore, in this paper, we are addressing the main issues in creating a machine translation system for the Kurdish language, with a focus on the Sorani dialect. We describe the available scarce parallel data suitable for training a neural machine translation model for Sorani Kurdish-English translation. We also discuss some of the major challenges in Kurdish language translation and demonstrate how fundamental text processing tasks, such as tokenization, can improve translation performance.