Central Kurdish machine translation: First large scale parallel corpus
  and experiments
  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.
