Central Kurdish machine translation: First large scale parallel corpus and experiments
This work addresses the problem of limited machine translation capabilities for Central Kurdish, a low-resource language, by providing foundational resources and benchmarks, though it is incremental in advancing the field.
The authors tackled the lack of resources for Central Kurdish machine translation by creating the first large-scale parallel corpus (Awta) with 229,222 manually aligned translations and built neural models to benchmark the task, achieving BLEU scores of 22.72 for Ku→EN and 16.81 for En→Ku.
While the computational processing of Kurdish has experienced a relative increase, the machine translation of this language seems to be lacking a considerable body of scientific work. This is in part due to the lack of resources especially curated for this task. In this paper, we present the first large scale parallel corpus of Central Kurdish-English, Awta, containing 229,222 pairs of manually aligned translations. Our corpus is collected from different text genres and domains in an attempt to build more robust and real-world applications of machine translation. We make a portion of this corpus publicly available in order to foster research in this area. Further, we build several neural machine translation models in order to benchmark the task of Kurdish machine translation. Additionally, we perform extensive experimental analysis of results in order to identify the major challenges that Central Kurdish machine translation faces. These challenges include language-dependent and-independent ones as categorized in this paper, the first group of which are aware of Central Kurdish linguistic properties on different morphological, syntactic and semantic levels. Our best performing systems achieve 22.72 and 16.81 in BLEU score for Ku$\rightarrow$EN and En$\rightarrow$Ku, respectively.