This paper presents a Function Word centered, Syntax-based (FWS) solution to address phrase ordering in the context of statistical machine translation (SMT). Motivated by the observation that function words often encode grammatical relationship among phrases within a sentence, we propose a probabilistic synchronous grammar to model the ordering of function words and their left and right arguments. We improve phrase ordering performance by lexicalizing the resulting rules in a small number of cases corresponding to function words. The experiments show that the FWS approach consistently outperforms the baseline system in ordering function words' arguments and improving translation quality in both perfect and noisy word alignment scenarios.
REF employ a "function-word centered syntax-based approach", with synchronous CFG and extended ITG models for reordering phrases, and relax syntactic constraints by only using a small number function words (approximated by high-frequency words) to guide the phrase-order inversion.
Ordering Phrases with Function Words
45th Annual Meeting of the Association of Computational Linguistics
Computer Science