Parallel FDA5 for fast deployment of accurate statistical machine translation systems

We use parallel FDA5, an efficiently parameterized and optimized parallel implementation of feature decay algorithms for fast deployment of accurate statistical machine translation systems, taking only about half a day for each translation direction. We build Parallel FDA5 Moses SMT systems for all language pairs in the WMT14 translation task and obtain SMT performance close to the top Moses systems with an average of $3.49$ BLEU points difference using significantly less resources for training and development

Quality expectations of machine translation

Machine Translation (MT) is being deployed for a range of use-cases by millions of people on a daily basis. There should, therefore, be no doubt as to the utility of MT. However, not everyone is convinced that MT can be useful, especially as a productivity enhancer for human translators. In this chapter, I address this issue, describing how MT is currently deployed, how its output is evaluated and how this could be enhanced, especially as MT quality itself improves. Central to these issues is the acceptance that there is no longer a single ‘gold standard’ measure of quality, such that the situation in which MT is deployed needs to be borne in mind, especially with respect to the expected ‘shelf-life’ of the translation itself

Experiments in Medical Translation Shared Task at WMT 2014

Abstract This paper describes Dublin City University's (DCU) submission to the WMT 2014 Medical Summary task. We report our results on the test data set in the French to English translation direction. We also report statistics collected from the corpora used to train our translation system. We conducted our experiment on the Moses 1.0 phrase-based translation system framework. We performed a variety of experiments on translation models, reordering models, operation sequence model and language model. We also experimented with data selection and removal the length constraint for phrase-pair extraction

Findings of the 2015 Workshop on Statistical Machine Translation

This paper presents the results of the WMT15 shared tasks, which included a standard news translation task, a metrics task, a tuning task, a task for run-time estimation of machine translation quality, and an automatic post-editing task. This year, 68 machine translation systems from 24 institutions were submitted to the ten translation directions in the standard translation task. An additional 7 anonymized systems were included, and were then evaluated both automatically and manually. The quality estimation task had three subtasks, with a total of 10 teams, submitting 34 entries. The pilot automatic postediting task had a total of 4 teams, submitting 7 entries