Syntactic machine translation systems extract rules from bilingual, word-aligned, syntactically parsed text, but current systems for parsing and word alignment are at best cascaded and at worst totally independent of one another. This work presents a unified joint model for simultaneous parsing and word alignment. To flexibly model syntactic divergence, we develop a discriminative log-linear model over two parse trees and an ITG derivation which is encouraged but not forced to synchronize with the parses. Our model gives absolute improvements of 3.3 F 1 for English parsing, 2.1 F 1 for Chinese parsing, and 5.5 F 1 for word alignment over each task's independent baseline, giving the best reported results for both Chinese-English word alignment and joint parsing on the parallel portion of the Chinese treebank. We also show an improvement of 1.2 BLEU in downstream MT evaluation over basic HMM alignments.
REF presented a supervised learning method for performing joint parsing and word alignment using log-linear models over parse trees and an ITG model over alignment.
Joint Parsing and Alignment with Weakly Synchronized Grammars
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Computer Science