Proceedings of the AMTA 2018 Workshop on Translation Quality Estimation and Automatic Post Editing

The goal of quality estimation is to evaluate a translation system’s quality without access to reference translations (Blatz et al., 2004; Specia et al., 2013). This has many potential usages: informing an end user about the reliability of translated content; deciding if a translation is ready for publishing or if it requires human post-editing; highlighting the words that need to be changed. Quality estimation systems are particularly appealing for crowd-sourced and professional translation services, due to their potential to dramatically reduce post-editing times and to save labor costs (Specia, 2011). The increasing interest in this problem from an industrial angle comes as no surprise (Turchi et al., 2014; de Souza et al., 2015; Martins et al., 2016, 2017; Kozlova et al., 2016). A related task is that of automatic post-editing (Simard et al.(2007), Junczys-Dowmunt and Grundkiewicz (2016)), which aims to automatically correct the output of machine translation. Recent work (Martins, 2017, Kim et al., 2017, Hokamp, 2017) has shown that the tasks of quality estimation and automatic post-editing benefit from being trained or stacked together.In this workshop, we will bring together researchers and industry practitioners interested in the tasks of quality estimation (word, sentence, or document level) and automatic post-editing, both from a research perspective and with the goal of applying these systems in industrial settings for routing, for improving translation quality, or for making human post-editors more efficient. Special emphasis will be given to the case of neural machine translation and the new open problems that it poses for quality estimation and automatic post …