*Call for Papers* 2nd Workshop on Human-aided translation (HAT) Co-located with MT-Summit, Dublin (Ireland), 19th August 2019 (https://sites.google.com/unbabel.com/hat19/home <https://sites.google.com/unbabel.com/hat19/home>)
With the recent advances in the machine translation era and the high quality translations obtained by neural MT systems, we observe human translators and MT systems changing their roles. Instead of using the MT outputs as the raw material to start the translation, human translators now just need to perform the very last touches on the automatic translations and send them to the end-users. The increased trust in MT quality, however, requires a more careful monitoring of MT systems in the production line in order to spot errors at the end of the translation pipeline and to fix them, either automatically or manually. In this pipeline, Quality, Cost, and Delivery speed are the three main factors. We ultimately want to preserve translation quality while increasing translation speed and keeping the final cost of translation in different scenarios under control. To this end, quality estimation and automatic post-editing solutions play important roles. The goal of quality estimation is to evaluate a translation system’s quality without access to the reference translations (Blatz et al., 2004; Specia et al., 2009). This has many potential uses: informing the end user about the reliability of translated content; deciding if a translation is ready for publishing or if it requires human post-editing; and 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 industry angle comes as no surprise (Federico et al., 2014; de Souza et al., 2015; Kozlova et al., 2016; Martins et al., 2016, 2017; Wang et al., 2018). Recently, it has also started to attract attention in the direct publishing scenario, mostly from e-commerce companies (Ueffing, 2018; Wang et al. 2018). Automatic post-editing, on the other hand, aims to automatically correct the output of machine translation (Simard et al. (2007), Junczys-Dowmunt and Grundkiewicz (2017, 2018)). Given the high quality translations obtained by neural MT systems, the key question is if quality estimation and automatic post-editing are still the thing! The workshop of “Human-aided Translation” builds upon the workshop of “First Workshop on Translation Quality Estimation and Automatic Post-Editing”, a successful and well-attended workshop recently held with AMTA 2018. It will bring together academic and industry researchers, as well as 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 industry settings for routing, for improving translation quality, or for making human post-editors more efficient. In this edition, we will give special emphasis to neural-based solutions for quality estimation and automatic post-editing tools and their integration with neural machine translation systems. *Submissions* We invite the submission of extended abstracts related to the topics of the workshop. The authors of the accepted submissions will be invited for contribution talks in the workshop. The abstracts should be no longer than two pages, including references. Topics of the workshop include but are not limited to: - Research, review, and position papers on document-level, sentence-level, or word-level Quality Estimation of neural MTs - Research, review, and position papers on Automatic Post-Editing for neural MTs - Research, review, and position papers on Interactive neural Mt - Corpora curation technologies for developing Quality Estimation datasets - User studies showing the impact of Quality Estimation tools in translator productivity - Automatic metrics for translation fluency and adequacy - Industrial experiences of adopting Quality Estimation for neural MTs - Industrial experiences of adopting Automatic Post-Editing for neural MTs Submissions should be formatted according to the ACL template (http://www.acl2019.org/medias/340-acl2019-latex.zip <http://www.acl2019.org/medias/340-acl2019-latex.zip>). The extended abstracts should be submitted via EasyChair system: https://easychair.org/conferences/?conf=hat19 <https://easychair.org/conferences/?conf=hat19>. Abstracts will be reviewed for relevance and quality. Accepted submissions will be posted online, and offered oral presentations. *Important dates* Submission deadline: May 31 Notification date: June 28 Workshop day: August 19 *Confirmed invited speakers* - Marco Turchi (FBK) - Lucia Specia (University fo Sheffield) - Marcin Junczys-Dowmunt (Microsoft) - Dimitar Shterionov (ADAPT Centre) - Markus Freitag (Google) *Organizers* Maxim Khalilov (Unbabel): ma...@unbabel.com <mailto:ma...@unbabel.com> M. Amin Farajian (Unbabel): a...@unbabel.com <mailto:a...@unbabel.com> André Martins (Unbabel): andre.mart...@unbabel.com <mailto:andre.mart...@unbabel.com>
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