CLARIFY: Human-Powered Training of SMT Models

D. Scott Appling, Ellen Yi-Luen Do

We present CLARIFY, an augmented environment that aims to improve the quality of translations generated by phrase-based statistical machine translation systems through learning from humans. CLARIFY employs four types of knowledge input: 1) direct input 2) results from either a word alignment game, 3) an phrase alignment game, or 4) a paraphrasing game. All of these knowledge inputs elicit user knowledge to improve future system translations.


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