  Neural Machine Translation with its significant results, still has a great
problem: lack or absence of parallel corpus for many languages. This article
suggests a method for generating considerable amount of parallel corpus for any
language pairs, extracted from open source materials existing on the Internet.
Parallel corpus contents will be derived from video subtitles. It needs a set
of video titles, with some attributes like release date, rating, duration and
etc. Process of finding and downloading subtitle pairs for desired language
pairs is automated by using a crawler. Finally sentence pairs will be extracted
from synchronous dialogues in subtitles. The main problem of this method is
unsynchronized subtitle pairs. Therefore subtitles will be verified before
downloading. If two subtitle were not synchronized, then another subtitle of
that video will be processed till it finds the matching subtitle. Using this
approach gives ability to make context based parallel corpus through filtering
videos by genre. Context based corpus can be used in complex translators which
decode sentences by different networks after determining contents subject.
Languages have many differences in their formal and informal styles, including
words and syntax. Other advantage of this method is to make corpus of informal
style of languages. Because most of movies dialogues are parts of a
conversation. So they had informal style. This feature of generated corpus can
be used in real-time translators to have more accurate conversation
translations.
