  Continuously-growing data volumes lead to larger generic models. Specific
use-cases are usually left out, since generic models tend to perform poorly in
domain-specific cases. Our work addresses this gap with a method for selecting
in-domain data from generic-domain (parallel text) corpora, for the task of
machine translation. The proposed method ranks sentences in parallel
general-domain data according to their cosine similarity with a monolingual
domain-specific data set. We then select the top K sentences with the highest
similarity score to train a new machine translation system tuned to the
specific in-domain data. Our experimental results show that models trained on
this in-domain data outperform models trained on generic or a mixture of
generic and domain data. That is, our method selects high-quality
domain-specific training instances at low computational cost and data size.
