Semantic Textual Similarity in Quality Estimation

Quality Estimation (QE) predicts the quality of machine translation output without the need for a reference translation. This quality can be defined differently based on the task at hand. In an attempt to focus further on the adequacy and informativeness of translations, we integrate features of semantic similarity into QuEst, a framework for QE feature extraction. By using methods previously employed in Semantic Textual Similarity (STS) tasks, we use semantically similar sentences and their quality scores as features to estimate the quality of machine translated sentences. Preliminary experiments show that finding semantically similar sentences for some datasets is difficult and time-consuming. Therefore, we opt to start from the assumption that we already have access to semantically similar sentences. Our results show that this method can improve the prediction of machine translation quality for semantically similar sentences.