Using Senses in HMM Word Alignment

Some of the most used models for statistical word alignment are the IBM models. Although these models generate acceptable alignments, they do not exploit the rich information found in lexical resources, and as such have no reasonable means to choose better translations for specific senses. We try to address this issue by extending the IBM HMM model with an extra hidden layer which represents the senses a word can take, allowing similar words to share similar output distributions. We test a preliminary version of this model on English-French data. We compare different ways of generating senses and assess the quality of the alignments relative to the IBM HMM model, as well as the generated sense probabilities, in order to gauge the usefulness in Word Sense Disambiguation.