Bilingual word alignment forms the foundation of current work on statistical machine translation. Standard wordalignment methods involve the use of probabilistic generative models that are complex to implement and slow to train. In this paper we show that it is possible to approach the alignment accuracy of the standard models using algorithms that are much faster, and in some ways simpler, based on basic word-association statistics. Bilingual word alignment is the first step of most current approaches to statistical machine translation. Although the best performing systems are "phrasebased" (see, for instance, Och and Ney (2004) or Koehn et al. (2003) ), possible phrase translations must first be extracted from word-aligned bilingual text segments. The standard approach to word alignment makes use of five translation models defined by Brown et al. (1993) , sometimes augmented by an HMM-based model or Och and Ney's "Model 6" (Och and Ney, 2003) . The best of these models can produce high accuracy alignments, at least when trained on a large parallel corpus of fairly direct translations in closely related languages. There are a number of ways in which these standard models are less than ideal, however. The higher-accuracy models are mathematically complex, and also difficult to train, as they do not factor in a way that permits a dynamic programming solution. It can thus take many hours of processing time on current standard computers to train the models and produce an alignment of a large parallel corpus. In this paper, we take a different approach to word alignment, based on the use of bilingual wordassociation statistics rather than the generative probabilistic framework that the IBM and HMM models use. In the end we obtain alignment algorithms that are much faster, and in some ways simpler, whose accuracy comes surprisingly close to the established probabilistic generative approach. The experiments reported here were carried out using data from the workshop on building and using parallel texts held at HLT-NAACL 2003 (Mihalcea and Pedersen, 2003) . For the majority of our experiments, we used a subset of the Canadian Hansards bilingual corpus supplied for the workshop, comprising 500,000 English-French sentences pairs, including 37 sentence pairs designated as "trial" data, and 447 sentence pairs designated as test data. The trial and test data have been manually aligned at the word level, noting particular pairs of words either as "sure" or "possible" alignments. As an additional test, we evaluated our best alignment method using the workshop corpus of approximately 49,000 English-Romanian sentences pairs from diverse sources, including 248 manually aligned sentence pairs designated as test data. 1
In REF , Moore proposes the log-likelihood-ratio association measure and alignment algorithm, which is faster and simpler than the generative probabilistic framework.
Association-Based Bilingual Word Alignment
Workshop On Building And Using Parallel Texts
Computer Science