We present a method to align words in a bitext that combines elements of a traditional statistical approach with linguistic knowledge. We demonstrate this approach for Arabic-English, using an alignment lexicon produced by a statistical word aligner, as well as linguistic resources ranging from an English parser to heuristic alignment rules for function words. These linguistic heuristics have been generalized from a development corpus of 100 parallel sentences. Our aligner, UALIGN, outperforms both the commonly used GIZA++ aligner and the state-of-theart LEAF aligner on F-measure and produces superior scores in end-to-end statistical machine translation, +1.3 BLEU points over GIZA++, and +0.7 over LEAF.
In REF , parse trees and a few phrase reordering heuristics were incorporated after using the alignment lexicon generated by a statistical word aligner.
Improved Word Alignment with Statistics and Linguistic Heuristics
EMNLP
Computer Science