Machine translation (MT) technology has facilitated our daily tasks by providing accessible shortcuts for gathering, processing, and communicating information. However, it can suffer from biases that harm users and society at large. As a relatively new field of inquiry, studies of gender bias in MT still lack cohesion. This advocates for a unified framework to ease future research. To this end, we: i) critically review current conceptualizations of bias in light of theoretical insights from related disciplines, ii) summarize previous analyses aimed at assessing gender bias in MT, iii) discuss the mitigating strategies proposed so far, and iv) point toward potential directions for future work.

Mattia A. Di Gangi, Roldano Cattoni, Luisa Bentivogli, Matteo Negri, Marco Turchi. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.

Automatic subtitling is the task of automatically translating the speech of audiovisual content into short pieces of timed text, i.e., subtitles and their corresponding timestamps. The generated subtitles need to conform to space and time requirements, while being synchronized with the speech and segmented in a way that facilitates comprehension. Given its considerable complexity, the task has so far been addressed through a pipeline of components that separately deal with transcribing, translating, and segmenting text into subtitles, as well as predicting timestamps. In this paper, we propose the first direct speech translation model for automatic subtitling that generates subtitles in the target language along with their timestamps with a single model. Our experiments on 7 language pairs show that our approach outperforms a cascade system in the same data condition, also being competitive with production tools on both in-domain and newly released out-domain benchmarks covering new scenarios.

We investigate adaptive machine translation (MT) as a way to reduce human workload and enhance user experience when professional translators operate in real-life conditions. A crucial aspect in our analysis is how to ensure a reliable assessment of MT technologies aimed to support human post-editing. We pay particular attention to two evaluation aspects: i) the design of a sound experimental protocol to reduce the risk of collecting biased measurements, and ii) the use of robust statistical testing methods (linear mixed-effects models) to reduce the risk of under/over-estimating the observed variations. Our adaptive MT technology is integrated in a web-based full-fledged computer-assisted translation (CAT) tool. We report on a post-editing field test that involved 16 professional translators working on two translation directions (English-Italian and English-French), with texts coming from two linguistic domains (legal, information technology). Our contrastive experiments compare user post-editing effort with static vs. adaptive MT in an end-to-end scenario where the system is evaluated as a whole. Our results evidence that adaptive MT leads to an overall reduction in post-editing effort (HTER) up to 10.6% (p <; 0.05). A follow-up manual evaluation of the MT outputs and their corresponding post-edits confirms that the gain in HTER corresponds to higher quality of the adaptive MT system and does not come at the expense of the final human translation quality. Indeed, adaptive MT shows to return better suggestions than static MT (p <; 0.01), and the resulting post-edits do not significantly differ in the two conditions.

This paper reports on the organization and results of the first Automatic Translation Memory Cleaning Shared Task. This shared task is aimed at finding automatic ways of cleaning translation memories (TMs) that have not been properly curated and thus include incorrect translations. As a follow up of the shared task, we also conducted two surveys, one targeting the teams participating in the shared task, and the other one targeting professional translators. While the researchers-oriented survey aimed at gathering information about the opinion of participants on the shared task, the translators-oriented survey aimed to better understand what constitutes a good TM unit and inform decisions that will be taken in future editions of the task. In this paper, we report on the process of data preparation and the evaluation of the automatic systems submitted, as well as on the results of the collected surveys.