Syntax-aware Neural Machine Translation Using CCG

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Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks (2017, background): “Other works proposed including linguistic characteristics such as such as part of speech tags [18] or syntactic dependency information [14] about the current word.”
Search Engine Guided Non-Parametric Neural Machine Translation (2017, background): “…et al., 2015; Dong et al., 2015), multi-source translation (Zoph and Knight, 2016; Firat et al., 2016b; Garmash and Monz, 2016), multimodal translation (Caglayan et al., 2016) and linguistic knowledge guided translation (Nadejde et al., 2017; Aharoni and Goldberg, 2017; Eriguchi et al., 2017).”
Search Engine Guided Neural Machine Translation (2018, background): “2016) and syntax guided translation (Nadejde et al. 2017; Eriguchi, Tsuruoka, and Cho 2017).”
Scheduled Multi-Task Learning: From Syntax to Translation (2018, result): “2016; Kuncoro et al., 2017; Niehues and Cho, 2017; Sennrich and Haddow, 2016; Eriguchi et al., 2017; Aharoni and Goldberg, 2017; Nadejde et al., 2017; Bastings et al., 2017; Matthews et al., 2018) has proven to be beneficial, achieving higher results in language modeling and machine translation.”
Modeling Target-Side Inflection in Neural Machine Translation (2017, methodology): “Following Nadejde et al. (2017), we set the maximum sequence length to 50 for the baseline and to 100 for systems which produce interleaved outputs.”