Shallow-Syntax Phrase-Based Translation: Joint versus Factored String-to-Chunk Models
Mauro Cettolo, Marcello Federico, Daniele Pighin, Nicola Bertoldi · 2008
This work extends phrase-based statistical MT (SMT) with shallow syntax dependencies. Two string-to-chunks translation models are proposed: a factored model, which augments phrase-based SMT with layered dependen-cies, and a joint model, that extends the phrase translation table with microtags, i.e. per-word projections of chunk labels. Both rely on n-gram models of target sequences with different granularity: single words, micro-tags, chunks. In particular, n-grams defined over syntactic chunks should model syntac-tic constraints coping with word-group move-ments. Experimental analysis and evaluation conducted on two popular Chinese-English tasks suggest that the shallow-syntax joint-translation model has potential to outperform state-of-the-art phrase-based translation, with a reasonable computational overhead. 1