Translating polysemous expressions

Polysemy is one of the most intricate features in machine translation. A word may have more than one meaning, and these meanings range from near-synonyms to entirely different concepts. Various machine translation systems have varying success rates in translating such structures. Google Translate (GT), the leading translation system, has constantly improved its performance in translating such structures. Its success is obviously based on model translations found in the Web. On the other hand, if such model translations are not found, GT fails badly. GT and other leading machine translation systems use currently neural methods, and it is practically impossible to trace the translation process phase by phase. Therefore, the work with neural approaches is counter-intuitive, because we cannot fix the translation mistakes in the correct phase of the translation process. Here I will discuss the translation of polysemic expressions in the context of rule- based machine translation. The translation system is modular, composed of several clearly ordered modules. This makes it possible to correct the mistakes on the optimal point of the translation chain. Examples are from English, Finnish, and German

Quantifying the effect of machine translation in a high-quality human translation production process

This paper studies the impact of machine translation (MT) on the translation workflow at the Directorate-General for Translation (DGT), focusing on two language pairs and two MT paradigms: English-into-French with statistical MT and English-into-Finnish with neural MT. We collected data from 20 professional translators at DGT while they carried out real translation tasks in normal working conditions. The participants enabled/disabled MT for half of the segments in each document. They filled in a survey at the end of the logging period. We measured the productivity gains (or losses) resulting from the use of MT and examined the relationship between technical effort and temporal effort. The results show that while the usage of MT leads to productivity gains on average, this is not the case for all translators. Moreover, the two technical effort indicators used in this study show weak correlations with post-editing time. The translators' perception of their speed gains was more or less in line with the actual results. Reduction of typing effort is the most frequently mentioned reason why participants preferred working with MT, but also the psychological benefits of not having to start from scratch were often mentioned

Findings of the 2019 Conference on Machine Translation (WMT19)

This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019. Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation

MORSE: Semantic-ally Drive-n MORpheme SEgment-er

We present in this paper a novel framework for morpheme segmentation which uses the morpho-syntactic regularities preserved by word representations, in addition to orthographic features, to segment words into morphemes. This framework is the first to consider vocabulary-wide syntactico-semantic information for this task. We also analyze the deficiencies of available benchmarking datasets and introduce our own dataset that was created on the basis of compositionality. We validate our algorithm across datasets and present state-of-the-art results