In this paper the goals and main objectives of the project MTUOC are presented. This project aims to ease the process of training and integrating neural machine translation (NMT) systems into professional translation environments. The MTUOC project distributes a series of auxiliary tools that allow to perform parallel corpus compilation and preprocessing, as well as the training of NMT systems. The project also distributes a server that implements most of the communication protocols used in computer assisted translation tools.

There are several parallel corpora available for many language pairs, such as CCMatrix, built from mass downloads of web content and automatic detection of segments in one language and the translation equivalent in another. These techniques can produce large parallel corpora, but of questionable quality. In many cases, the segments are not in the required languages, or if they are, they are not translation equivalents. In this article, we present an algorithm for filtering out the segments in languages other than the required ones and re-scoring the segments using SBERT. A use case on the Spanish-Asturian and Spanish-Catalan CCMatrix corpus is presented.

Neural machine translation (NMT) has shown overwhelmingly good results in recent times. This improvement in quality has boosted the presence of NMT in nearly all fields of translation. Most current translation industry workflows include postediting (PE) of MT as part of their process. For many domains and language combinations, translators post-edit raw machine translation (MT) to produce the final document. However, this process can only work properly if the quality of the raw MT output can be assured. MT is usually evaluated using automatic scores, as they are much faster and cheaper. However, traditional automatic scores have not been good quality indicators and do not correlate with PE effort. We analyze the correlation of each of the three dimensions of PE effort (temporal, technical and cognitive) with COMET, a neural framework which has obtained outstanding results in recent MT evaluation campaigns.

The main goal of this project is to explore the techniques for training NMT systems applied to Spanish, Portuguese, Catalan, Galician, Asturian, Aragonese and Aranese. These languages belong to the same Romance family, but they are very different in terms of the linguistic resources available. Asturian, Aragonese and Aranese can be considered low resource languages. These characteristics make this setting an excellent place to explore training techniques for low-resource languages: transfer learning and multilingual systems, among others. The first months of the project have been dedicated to the compilation of monolingual and parallel corpora for Asturian, Aragonese and Aranese.

There is currently an extended use of post-editing of machine translation (PEMT) in the translation industry. This is due to the increase in the demand of translation and to the significant improvements in quality achieved by neural machine translation (NMT). PEMT has been included as part of the translation workflow because it increases translators’ productivity and it also reduces costs. Although an effective post-editing requires enough quality of the MT output, usual automatic metrics do not always correlate with post-editing effort. We describe a standalone tool designed both for industry and research that has two main purposes: collect sentence-level information from the post-editing process (e.g. post-editing time and keystrokes) and visually present multiple evaluation scores so they can be easily interpreted by a user.

The recent improvements in machine translation (MT) have boosted the use of post-editing (PE) in the translation industry. A new machine translation paradigm, neural machine translation (NMT), is displacing its corpus-based predecessor, statistical machine translation (SMT), in the translation workflows currently implemented because it usually increases the fluency and accuracy of the MT output. However, usual automatic measurements do not always indicate the quality of the MT output and there is still no clear correlation between PE effort and productivity. We present a quantitative analysis of different PE effort indicators for two NMT systems (transformer and seq2seq) for English-Spanish in-domain medical documents. We compare both systems and study the correlation between PE time and other scores. Results show less PE effort for the transformer NMT model and a high correlation between PE time and keystrokes.