Hidden Markov Tree Model for Word Alignment
Shuhei Kondo, Kevin Duh, Yūji Matsumoto · 2013
We propose a novel unsupervised word alignment model based on the Hidden Markov Tree (HMT) model. Our model assumes that the alignment variables have a tree structure which is isomorphic to the target dependency tree and models the dis-tortion probability based on the source de-pendency tree, thereby incorporating the syntactic structure from both sides of the parallel sentences. In English-Japanese word alignment experiments, our model outperformed an IBM Model 4 baseline by over 3 points alignment error rate. While our model was sensitive to poste-rior thresholds, it also showed a perfor-mance comparable to that of HMM align-ment models. 1