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.

Background: A question that deserves to be explored is whether the interaction between English language learners and the popular Google neural machine translation (GNMT) system could result in learning and increased production of a challenging syntactic structure in English that differs in word order between speakers first language and second language. Methods: In this paper, we shed light on this issue by testing 30 Brazilian Portuguese L2 English speakers in order to investigate whether they tend to describe an image in English with a relation of possession between nouns using a prepositional noun phrase (e.g. the cover of the book is red) or re-use the alternative syntactic structure seen in the output of the GNMT (e.g. the book cover is red), thus manifesting syntactic priming effects. In addition, we tested whether, after continuous exposure to the challenging L2 structure through Google Translate output, speakers would adapt to that structure in the course of the experiment, thus manifesting syntactic priming cumulative effects. Results: Our results show a robust syntactic priming effect as well as a robust cumulative effect. Conclusions: These results suggest that GNMT can influence L2 English learners linguistic behaviour and that L2 English learners unconsciously learn from the GNMT with continuous exposure to its output.

In the present study, we investigated the post-editese phenomenon, i.e., the unique features that set machine translated post-edited texts apart from human-translated texts. We used two literary texts, namely, the English children’s novel by Lewis Carroll Alice’s Adventures in Wonderland (AW) and Paula Hawkins’ popular book The Girl on the Train (TGOTT). Both literary texts were Google translated from English into Brazilian Portuguese to investigate whether the post-editese features can be found on the surface of the post-edited (PE) texts. In addition, we examined how the features found in the PE texts differ from the features encountered in the human-translated (HT) and machine translation (MT) versions of the same source text. Results revealed evidence for post-editese for TGOTT only with PE versions being more similar to the MT output than to the HT texts.

Este artigo apresenta um estudo comportamental que teve como objetivo investigar se substantivos inanimados do português do Brasil, transparentes quanto ao seu gênero gramatical (substantivos femininos terminados em -a e masculinos terminados em -o) e opacos (outras terminações) são ou não processados por mecanismos cognitivos distintos. Para tanto, uma tarefa de concordância de gênero foi executada por 19 sujeitos em duas condições: entre um artigo definido e um substantivo (condição 1) e entre um substantivo e um adjetivo (condição 2). Fatores como a frequência dos substantivos e adjetivos (alta vs. baixa) e forma fonológica (transparente vs. opaca) foram manipulados. Os resultados mostram que, em ambas as condições, a frequência é um forte preditor de respostas mais rápidas, sugerindo que tanto formas transparentes quanto formas opacas podem ser armazenadas na memória. Este padrão foi encontrado em ambas as condições. Estes resultados foram interpretados como evidência para a visão unitária de processamento da linguagem.

In the present study, we investigate the post-editese phenomenon, i.e., the unique features that set machine translated post-edited texts apart from human-translated texts. We use two literary texts, namely, the English children's novel by Lewis Carroll Alice’s Adventures in Wonderland (AW) and Paula Hawkins' popular book The Girl on the Train (TGOTT) translated from English into Brazilian-Portuguese to investigate whether the post-editese features can be found on the surface of the post-edited (PE) texts. In addition, we examine how the features found in the PE texts differ from the features encountered in the human-translated (HT) and machine translation (MT) versions of the same source text. Results revealed evidence for post-editese for TGOTT only with PE versions being more similar to the MT output than to the HT texts.

The present study used event-related potentials to investigate whether the processing of grammatical gender agreement involving gender regular and irregular forms recruit the same or distinct neurocognitive mechanisms and whether different grammatical gender agreement conditions elicit the same or diverse ERP signals. Native speakers of Brazilian Portuguese read sentences containing congruent and incongruent grammatical gender agreement between a determiner and a regular or an irregular form (condition 1) and between a regular or an irregular form and an adjective (condition 2). However, in condition 2, trials with incongruent regular forms elicited more positive ongoing waveforms than trial with incongruent irregular forms. We found a biphasic LAN/P600 effect for gender agreement violation involving regular and irregular forms in both conditions. Our findings suggest that gender agreement between determiner and nouns recruits the same neurocognitive mechanisms regardless of the nouns' form and that, depending on the grammatical class of the words involved in gender agreement, differences in ERP signals can emerge.

In this article, we address the question of whether exposure to the translated output of MT systems could result in changes in the cognitive processing of English as a second language (L2 English). To answer this question, we first conducted a survey with 90 Brazilian Portuguese L2 English speakers with the aim of understanding how and for what purposes they use web-based MT systems. To investigate whether MT systems are capable of influencing L2 English cognitive processing, we carried out a syntactic priming experiment with 32 Brazilian Portuguese speakers. We wanted to test whether speakers re-use in their subsequent speech in English the same syntactic alternative previously seen in the MT output, when using the popular Google Translate system to translate sentences from Portuguese into English. The results of the survey show that Brazilian Portuguese L2 English speakers use Google Translate as a tool supporting their speech in English as well as a source of English vocabulary learning. The results of the syntactic priming experiment show that exposure to an English syntactic alternative through GT can lead to the re-use of the same syntactic alternative in subsequent speech even if it is not the speaker’s preferred syntactic alternative in English. These findings suggest that GT is being used as a tool for language learning purposes and so is indeed capable of rewiring the processing of L2 English syntax.

Within the ELE project three complementary online surveys were designed and implemented to consult the Language Technology (LT) community with regard to the current state of play and the future situation in about 2030 in terms of Digital Language Equality (DLE). While Chapters 4 and 38 provide a general overview of the community consultation methodology and the results with regard to the current situation as of 2022, this chapter summarises the results concerning the future situation in 2030. All of these results have been taken into account for the specification of the project’s Strategic Research, Innovation and Implementation Agenda (SRIA) and Roadmap for Achieving Full DLE in Europe by 2030.