Improved Word Alignment with Statistics and Linguistic Heuristics

Ulf Hermjakob

Abstract: 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-the-art 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.