DeepSubQE: Quality estimation for subtitle translations
This work addresses quality estimation for subtitle translations, which is a domain-specific problem for video content creators and translators, and is incremental as it builds on existing QE methods with a novel hybrid approach.
The paper tackled the problem of quality estimation for subtitle translations by proposing DeepSubQE, a hybrid network that learns semantic and syntactic features from bilingual data, which outperformed LSTM and CNN baselines by a significant margin.
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.