In this paper, we analyse to what extent machine translation (MT) systems and humans base their gender translations and associations on role names and on stereotypicality in the absence of (generic) grammatical gender cues in language. We compare an MT system’s choice of gender for a certain word when translating from a notional gender language, English, into a grammatical gender language, German, with thegender associations of humans. We outline a comparative case study of gender translation and annotation of words in isolation, out-of-context, and words in sentence contexts. The analysis reveals patterns of gender (bias) by MT and gender associations by humans for certain (1) out-of-context words and (2) words in-context. Our findings reveal the impact of context on gender choice and translation and show that word-level analyses fall short in such studies.

Gender-inclusive translations are the default at the International Quadball Association, yet translators make different choices for the (timed) referee certification tests to improve readability. However, the actual impact of a strategy on readability and performance has not been tested. This pilot study explores the impact of translation strategy (masculine generic, gender-inclusive, and machine translation) on the speed, performance and perceptions of quadball referee test takers in German. It shows promise for inclusive over masculine strategies, and suggests limited usefulness of MT in this context.

This research project aims to develop a comprehensive methodology to help make machine translation (MT) systems more gender-inclusive for society. The goal is the creation of a detection system, a machine learning (ML) model trained on manual annotations, that can automatically analyse source data and detect and highlight words and phrases that influence the gender bias inflection in target translations.The main research outputs will be (1) a manually annotated dataset, (2) a taxonomy, and (3) a fine-tuned model.

The use of machine translation is increasingly being explored for the translation of literary texts, but there is still a lot of uncertainty about the optimal translation workflow in these scenarios. While overall quality is quite good, certain textual characteristics can be different in a human translated text and a text produced by means of machine translation post-editing, which has been shown to potentially have an impact on reader perceptions and experience as well. In this study, we look at textual characteristics from short story translations from B.J. Novak’s One more thing into Dutch. Twenty-three professional literary translators translated three short stories, in three different conditions: using Word, using the classic CAT tool Trados, and using a machine translation post-editing platform specifically designed for literary translation. We look at overall text characteristics (sentence length, type-token ratio, stylistic differences) to establish whether translation workflow has an impact on these features, and whether the three workflows lead to very different final translations or not.

DUAL-T is an EU-funded project which aims at involving literary translators in the testing of technology-inclusive workflows. Participants will be asked to translate three short stories using, respectively, (1) a text editor combined with online resources, (2) a Computer-Aided Translation (CAT) tool, and (3) a Machine Translation Post-editing (MTPE) tool.

Gender-inclusive language is of key importance to the IQA, the international governing body for quadball, a mixed-gender contact sport that explicitly welcomes players of all genders. While relatively straightforward for English, the picture becomes more complicated for most of the other IQA working languages. This paper provides an overview of the strategies currently chosen by translation team leaders for different IQA languages, the factors that influenced this decision and their connection with existing research on inclusive language strategies. It further explores the awareness and attitudes of IQA translators towards those strategies and factors.

This study examines the effectiveness of adaptive machine translation (AMT) for gender-neutral language (GNL) use in English-German translation using the ModernMT engine. It investigates gender bias in initial output and adaptability to two distinct GNL strategies, as well as the influence of translation memory (TM) use on adaptivity. Findings indicate that despite inherent gender bias, machine translation (MT) systems show potential for adapting to GNL with appropriate exposure and training, highlighting the importance of customisation, exposure to diverse examples, and better representation of different forms for enhancing gender-fair translation strategies.

In the present paper, we describe a large corpus of eye movement data, collected during natural reading of a human translation and a machine translation of a full novel. This data set, called GECO-MT (Ghent Eye tracking Corpus of Machine Translation) expands upon an earlier corpus called GECO (Ghent Eye-tracking Corpus) by Cop et al. (2017). The eye movement data in GECO-MT will be used in future research to investigate the effect of machine translation on the reading process and the effects of various error types on reading. In this article, we describe in detail the materials and data collection procedure of GECO-MT. Extensive information on the language proficiency of our participants is given, as well as a comparison with the participants of the original GECO. We investigate the distribution of a selection of important eye movement variables and explore the possibilities for future analyses of the data. GECO-MT is freely available at https://www.lt3.ugent.be/resources/geco-mt.

This paper presents the project initiated by the BiasByUs team resulting from the 2021 Artificially Correct Hackaton. We briefly explain our winning participation in the hackaton, tackling the challenge on ‘Database and detection of gender bi-as in A.I. translations’, we highlight the importance of gender bias in Machine Translation (MT), and describe our pro-posed solution to the challenge, the cur-rent status of the project, and our envi-sioned future collaborations and re-search.

The WiLMa project aims to assess the effects of using machine translation (MT) tools on the writing processes of second language (L2) learners of varying proficiency. Particular attention is given to individual variation in learners’ tool use.

The ArisToCAT project aims to assess the comprehensibility of ‘raw’ (unedited) MT output for readers who can only rely on the MT output. In this project description, we summarize the main results of the project and present future work.

In order to improve the symbiosis between machine translation (MT) system and post-editor, it is not enough to know that the output of one system is better than the output of another system. A fine-grained error analysis is needed to provide information on the type and location of errors occurring in MT and the corresponding errors occurring after post-editing (PE). This article reports on a fine-grained translation quality assessment approach which was applied to machine translated-texts and the post-edited versions of these texts, made by student post-editors. By linking each error to the corresponding source text-passage, it is possible to identify passages that were problematic in MT, but not after PE, or passages that were problematic even after PE. This method provides rich data on the origin and impact of errors, which can be used to improve post-editor training as well as machine translation systems. We present the results of a pilot experiment on the post-editing of newspaper articles and highlight the advantages of our approach.