Controlling Neural Machine Translation Formality with Synthetic Supervision
Table 4: Heuristic analysis of the differences between informal and formal translations. Synthetic supervision introduce more changes. Online Target Inference usually performs simple substitutions while Online Style Inference performs more less-deterministic changes. Online Style Inference generates more complete and longer formal translations.
['Model', 'identical', 'contr.', 'filler', 'quot.', 'poss.', 'y/n', 'Δlength']
We first check how often formal and informal translations are identical. A sentence can be made more formal by expanding contractions (contr.) and removing unnecessary fillers such as conjunctions ( so/and/but) and interjections (well) at the beginning of a sentence (filler). Online Target Inference performs these changes more frequently. We also examine the introduction of quotation marks in formal translations (quot.); using possessive of instead of possessive ’s (poss.); and rewrites of informal use of declarative form for yes-no questions (y/n). Online Style Inference output matches these patterns better than other systems. Next, we conduct a manual analysis to understand the nature of remaining differences between formal and informal translations of Online Style Inference. We observe that ellipsis is frequent in informal outputs, while formal sentences are more complete, using complement subjects, proper articles, conjunctions, relative pronouns, etc. This is reflected in their longer length Lexical or phrasal paraphrases are frequently used to convey formality, substituting familiar terms with more formal variants (e.g., “grandma” vs. “grandmother”). Examining translations with large PoD scores shows that Online Style Inference is more likely to reorder adverbs based on formality: e.g., “I told you already” (I) vs. “I already told you” (F).