Grammar Comparison Study for Translational Equivalence Modeling and Statistical Machine Translation


This paper presents a general platform, namely synchronous tree sequence substitution grammar (STSSG), for the grammar comparison study in Translational Equivalence Modeling (TEM) and Statistical Machine Translation (SMT). Under the STSSG platform, we compare the expressive abilities of various grammars through synchronous parsing and a real translation platform on a variety of Chinese-English bilingual corpora. Experimental results show that the STSSG is able to better explain the data in parallel corpora than other grammars. Our study further finds that the complexity of structure divergence is much higher than suggested in literature, which imposes a big challenge to syntactic transformation-based SMT.