When developing Machine Translation engines, low resourced language pairs tend to be in a disadvantaged position: less available data means that developing robust MT models can be more challenging.The EU-funded PRINCIPLE project aims at overcoming this challenge for four low resourced European languages: Norwegian, Croatian, Irish and Icelandic. This presentation will give an overview of the project, with a focus on the set of Public Sector users and their use cases for which we have developed MT solutions.We will discuss the range of language resources that have been gathered through contributions from public sector collaborators, and present the extensive evaluations that have been undertaken, including significant user evaluation of MT systems across all of the public sector participants in each of the four countries involved.

In this paper, we tested 20 Brazilian Portuguese speakers at intermediate and advanced English proficiency levels to investigate the influence of Google Translate’s MT system on the mental processing of English as a second language. To this end, we employed a syntactic priming experimental paradigm using a pretest-priming design which allowed us to compare participants’ linguistic behaviour before and after a translation task using Google Translate. Results show that, after performing a translation task with Google Translate, participants more frequently described images in English using the syntactic alternative previously seen in the output of Google Translate, compared to the translation task with no prior influence of the MT output. Results also show that this syntactic priming effect is modulated by English proficiency levels.

Over the last decades, massive research investments have been made in the development of machine translation (MT) systems (Gupta and Dhawan, 2019). This has brought about a paradigm shift in the performance of these language tools, leading to widespread use of popular MT systems (Gaspari and Hutchins, 2007). Although the first MT engines were used for gisting purposes, in recent years, there has been an increasing interest in using MT tools, especially the freely available online MT tools, for language teaching and learning (Clifford et al., 2013). The literature on MT and Computer Assisted Language Learning (CALL) shows that, over the years, MT systems have been facilitating language teaching and also language learning (Nin ̃o, 2006). It has been shown that MT tools can increase awareness of grammatical linguistic features of a foreign language. Research also shows the positive role of MT systems in the development of writing skills in English as well as in improving communication skills in English(Garcia and Pena, 2011). However, to date, the cognitive impact of MT on language acquisition and on the syntactic aspects of language processing has not yet been investigated and deserves further scrutiny. The MTril project aims at filling this gap in the literature by examining whether MT is contributing to a central aspect of language acquisition: the so-called language binding, i.e., the ability to combine single words properly in a grammatical sentence (Heyselaar et al., 2017; Ferreira and Bock, 2006). The project focus on the initial stages (pre-intermediate and intermediate) of the acquisition of English syntax by Brazilian Portuguese native speakers using MT systems as a support for language learning.

While a number of studies have shown evidence of translationese phenomena, that is, statistical differences between original texts and translated texts (Gellerstam, 1986), results of studies searching for translationese features in postedited texts (what has been called ”posteditese” (Daems et al., 2017)) have presented mixed results. This paper reports a preliminary study aimed at identifying the presence of post-editese features in machine-translated post-edited texts and at understanding how they differ from translationese features. We test the influence of factors such as post-editing (PE) levels (full vs. light), translation proficiency (professionals vs. students) and text domain (news vs. literary). Results show evidence of post-editese features, especially in light PE texts and in certain domains.