Controlling the Voice of a Sentence in Japanese-to-English Neural Machine Translation

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Tagged Back-Translation (2019, methodology): “Yamagishi et al. (2016) use tags in a similar fashion to control the formality of a translation from English to Japanese.”
Case Study: Deontological Ethics in NLP (2020, background): “, 2015; Mirkin and Meunier, 2015) while preserving author attributes as well as controlling structural information like voice (Yamagishi et al., 2016).”
Translationese as a Language in “Multilingual” NMT (2019, methodology): “, 2016), formality level in English→Japanese (Yamagishi et al., 2016), politeness in English→German (Sennrich et al.”
Towards Content Transfer through Grounded Text Generation (2019, background): “…for various form-related and linguistic properties, such as style (Ficler and Goldberg, 2017), affect (Ghosh et al., 2017), politeness (Sennrich et al., 2016), persona (Li et al., 2016b) voice (Yamagishi et al., 2016), grammatical correctness (Ji et al., 2017), and length (Kikuchi et al., 2016).”
Transfer Learning in Multilingual Neural Machine Translation with Dynamic Vocabulary (2018, background): “Inspired by [24], who automatically annotated the source side with artificial flags to manage the politeness level of the output, other works focused on controlling the grammatical voice [25], the text domain [26, 27], and enforcing gender agreement [28].”