The limited semantic knowledge is used in the phrase-based statistical machine translation (SMT), which causes that the translation quality of long-distance verb and its object is low. A selectional preference based translation model is proposed, which inducts the semantic constraints that a verb imposes on its object to select the proper argument-head word for the predicate with long distance. The authors train the corpus to obtain the conditional probability based selectional preferences for verb, and integrate the selectional preferences into a phrase-based translation system and evaluate on a Chinese-to-English translation task with large-scale training data. Experiment results show that the integration of selectional preference into SMT can effectively capture the long-distance semantic dependencies and improve the translation quality. 

The authors propose a translation similarity model based on bilingual compositional semantics to integrate the bilingual semantic similarity feature into decoding process to improve translation quality. In the proposed model, monolingual compositional vectors for phrases are obtained at the source and target side respectively using distributional approach. These monolingual vectors are then projected onto the same semantic space and therefore transformed into bilingual compositional vectors. Base on this semantic space, translation similarity between source phrases and their corresponding target phrases is calculated. The similarities are integrated into the decoder as a new feature. Experiments on Chinese-to-English NIST06 and NIST08 test sets show that the proposed model significantly outperforms the baseline by 0.56 and 0.42 BLEU points respectively.