The Operation Sequence Model—Combining N-Gram-Based and Phrase-Based Statistical Machine Translation

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What do Neural Machine Translation Models Learn about Morphology? (2017, methodology): “…deal with morphologically-rich languages resort to various techniques for representing morphological knowledge, such as word segmentation (Nieflen and Ney, 2000; Koehn and Knight, 2003; Badr et al., 2008) and factored translation and reordering models (Koehn and Hoang, 2007; Durrani et al., 2015).”
EDITOR: An Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints (2020, background): “, 2007), or the Operation Sequence Model (Durrani et al., 2015; Stahlberg et al., 2018), which views translation as a sequence of translation and reordering operations over bilingual minimal units.”
On the Linguistic Representational Power of Neural Machine Translation Models (2019, background): “For example, non-neural statistical MT systems have sub-components to handle fluency (Heafield 2011), lexical generation (Koehn, Och, and Marcu 2003), word reordering (Galley and Manning 2008; Durrani et al. 2015), rich morphology (Koehn and Hoang 2007; Durrani et al.”
USAAR: An Operation Sequential Model for Automatic Statistical Post-Editing (2016, methodology): “We inspired from the work of Durrani et al. (2011) and Durrani et al. (2015).”