Informative quality estimation of machine translation output

Despite the recent advances in the field of machine translation (MT), today, MT systems cannot guarantee that the sentences they produce will be fluent and coherent in both syntax and semantics. With post-editing of MT output becoming a common practice in fast-paced Computer-Assisted Translation (CAT) workflows, research on Quality Estimation (QE) has thrived in recent years. Despite the link between MT errors and the cognitive effort involved in correcting them, current QE studies often focus on finding informative features that capture the monolingual and bilingual properties of given source/MT output pairs and estimate overall post-editing effort at word, sentence or document level without making a distinction between MT error types. In this thesis, we present a comprehensive approach to automatic error detection as a basis for understanding the relationship between different types of MT errors and the corresponding post-editing effort and take a first step towards informative quality estimation systems of machine translation, which are able to justify the basis for estimated quality. In order to study the relationship between MT errors and post-editing effort on a large scale, we developed an error taxonomy and a corpus of MT errors originating from statistical (SMT), rule-based and neural machine translation systems for English-Dutch and obtained post-edited versions of the MT output of this corpus. The error taxonomy is grounded in translation quality assessment literature and allows for an MT-specific, fine-grained error annotation based on the main distinction between accuracy and fluency errors. Moreover, the hier-