As an interdisciplinary area of research, translation studies attracts students and scholars
with a wide range of backgrounds, who then need to face the challenge of accounting for a …

Within the field of Statistical Machine Translation (SMT), the neural approach (NMT) has
recently emerged as the first technology able to challenge the long-standing dominance of …

This chapter presents an overview of different approaches and tasks related to classification
and analysis of errors in machine translation (MT) output. Manual error classification is a …

This article reports a multifaceted comparison between statistical and neural machine
translation (MT) systems that were developed for translation of data from massive open …

Within the field of statistical machine translation, the neural approach (NMT) is currently
pushing ahead the state of the art performance traditionally achieved by phrase-based …

This work presents the new flexible Multidimensional Quality Metrics (MQM) framework and
uses it to analyze the performance of state-of-the-art machine translation systems, focusing …

Translation process research (TPR) efforts seem at times unconcerned with the theoretical
foundations they need to interpret their results. A pervasive theoretical approach within TPR …

Addressing the global increase in social media users, platforms such as Instagram
introduced automatic translation to broaden information dissemination and improve cross …

Quality estimation (qe) and error analysis of machine translation (mt) output remain active
areas in Natural Language Processing (nlp) research. Many recent efforts have focused on …

Learning from errors is a crucial aspect of improving expertise. Based on this notion, we
discuss a robust statistical framework for analysing the impact of different error types on …