Hidden Markov Tree Model for Word Alignment
نویسندگان
چکیده
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 distortion probability based on the source dependency 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 posterior thresholds, it also showed a performance comparable to that of HMM alignment models.
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