  Quality estimation (QE) for tasks involving language data is hard owing to
numerous aspects of natural language like variations in paraphrasing, style,
grammar, etc. There can be multiple answers with varying levels of
acceptability depending on the application at hand. In this work, we look at
estimating quality of translations for video subtitles. We show how existing QE
methods are inadequate and propose our method DeepSubQE as a system to estimate
quality of translation given subtitles data for a pair of languages. We rely on
various data augmentation strategies for automated labelling and synthesis for
training. We create a hybrid network which learns semantic and syntactic
features of bilingual data and compare it with only-LSTM and only-CNN networks.
Our proposed network outperforms them by significant margin.
