We present the first application of the adjunct/argument distinction to Hierarchical Phrase-Based SMT. We use rule labelling to characterize synchronous recursion with adjuncts and arguments. Our labels are bilingual obtained from dependency annotations and extended to cover non-syntactic phrases. The label set we derive in this manner is extremely small, as it contains only thirty-six labels, and yet we find it useful to cluster these labels even further. We present a clustering method that uses label similarity based on left-hand-side/right-hand-side joint trained-model estimates. The results of initial experiments show that our model performs similarly to Hiero on in-domain French-English data.
展开▼
