Improving Statistical Word Alignment with a Rule-Based Machine Translation System

Improving Statistical Word Alignment with a Rule-Based Machine Translation System WU Hua, WANG Haifeng Toshiba (China) Research & Development Center 5/F., Tower W2, Oriental Plaza, No.1, East Chang An Ave., Dong Cheng District Beijing, China, 100738 {wuhua, wanghaifeng}@rdc.toshiba.com.cn Abstract The main problems of statistical word alignment lie in the facts that source words can only be aligned to one target word, and that the inappro- priate target word is selected because of data sparseness problem. This paper proposes an ap- proach to improve statistical word alignment with a rule-based translation system. This ap- proach first uses IBM statistical translation model to perform alignment in both directions (source to target and target to source), and then uses the translation information in the rule-based machine translation system to improve the statis- tical word alignment. The improved alignments allow the word(s) in the source language to be aligned to one or more words in the target lan- guage. Experimental results show a significant improvement in precision and recall of word alignment. 1 Introduction Bilingual word alignment is first introduced as an intermediate result in statistical machine transla- tion (SMT) (Brown et al. 1993). Besides being used in SMT, it is also used in translation lexicon building (Melamed 1996), transfer rule learning (Menezes and Richardson 2001), example-based machine translation (Somers 1999), etc. In previ- ous alignment methods, some researches mod- eled the alignments as hidden parameters in a statistical translation model (Brown et al. 1993; Och and Ney 2000) or directly modeled them given the sentence pairs (Cherry and Lin 2003). Some researchers used similarity and association measures to build alignment links (Ahrenberg et al. 1998; Tufis and Barbu 2002). In addition, Wu (1997) used a stochastic inversion transduction grammar to simultaneously parse the sentence pairs to get the word or phrase alignments. Generally speaking, there are four cases in word alignment: word to word alignment, word to multi-word alignment, multi-word to word alignment, and multi-word to multi-word align- ment. One of the most difficult tasks in word alignment is to find out the alignments that in- clude multi-word units. For example, the statisti- cal word alignment in IBM translation models (Brown et al. 1993) can only handle word to word and multi-word to word alignments. Some studies have been made to tackle this problem. Och and Ney (2000) performed transla- tion in both directions (source to target and target to source) to extend word alignments. Their re- sults showed that this method improved precision without loss of recall in English to German align- ments. However, if the same unit is aligned to two different target units, this method is unlikely to make a selection. Some researchers used preprocessing steps to identity multi-word units for word alignment (Ahrenberg et al. 1998; Tiedemann 1999; Melamed 2000). The methods obtained multi-word candidates based on con- tinuous N-gram statistics. The main limitation of these methods is that they cannot handle sepa- rated phrases and multi-word units in low fre- quencies. In order to handle all of the …

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