A Snapshot into the Possibility of Video Game Machine Translation
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Abstract (arXiv)
We present in this article what we believe to be one of the first attempts at video game machine translation. Our study shows that models trained only with limited in-domain data surpass publicly available systems by a significant margin, and a subsequent human evaluation reveals interesting findings in the final translation. The first part of the article introduces some of the challenges of video game translation, some of the existing literature, as well as the systems and data sets used in this experiment. The last sections discuss our analysis of the resulting translation and the potential benefits of such an automated system. One such finding highlights the model's ability to learn typical rules and patterns of video game translations from English into French. Our conclusions therefore indicate that the specific case of video game machine translation could prove very much useful given the encouraging results, the highly repetitive nature of the work, and the often poor working conditions that translators face in this field. As with other use cases of MT in cultural sectors, however, we believe this is heavily dependent on the proper implementation of the tool, which should be used interactively by human translators to stimulate creativity instead of raw post-editing for the sake of productivity.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 44 references that are in the map. First posted on arXiv 19 Sep 2022.
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References · 8
- 2017Attention Is All You Needcited by 59,932
- 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
- 2018A Call for Clarity in Reporting BLEU Scorescited by 1,790
- 2018Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidatescited by 632
- 2017OpenNMT: Open-source Toolkit for Neural Machine Translationcited by 251
- 2021Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Paperscited by 70
- 2018A Survey of Domain Adaptation for Neural Machine Translationcited by 64
- 2021Neural machine translation, corpus and frugalitycited by 2
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