Paper: Improving Statistical Word Alignment With A Rule-Based Machine Translation System

ACL ID C04-1005
Title Improving Statistical Word Alignment With A Rule-Based Machine Translation System
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2004
Authors

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...