Error detection and error correction for improving quality in machine translation and human post-editing

Machine translation (MT) has been an important field of research in the last decades and is currently playing a key role in the translation market. The variable quality of results makes it necessary to combine MT with post-editing, to obtain high-quality translation. Post-editing is, however, a costly and time-consuming task. Additionally, it is possible to improve the results by inte-grating more information in automatic systems. In order to improve automatic systems performance, it is crucial to evaluate the quality of results produced by MT systems to identify the main errors. In this study, we assessed the results of MT using an error-annotated corpus of texts translated from English into Ital-ian. The data collected allowed us to identify frequent and critical errors. De-tecting and correcting such errors would have a major impact on the quality of translation and make the post-editing process more accurate and efficient. The errors were analyzed in order to identify patterns of errors, and solutions to ad-dress them automatically or semi-automatically are presented. To achieve this a set of rules are formulated and integrated in a tool which detects or corrects the most frequent and critical errors in the texts.