Learning Hierarchical Translation Spans

Abstract

We propose a simple and effective approach to learn translation spans for the hierarchical phrase-based translation model. Our model evaluates if a source span should be covered by translation rules during decoding, which is integrated into the translation system as soft constraints.Compared to syntactic constraints, our model is directly acquired from an aligned parallel corpus and does not require parsers. Rich source side contextual features and advanced machine learning methods were utilized for this learning task. The proposed approach was evaluated on NTCIR-9 Chinese-English and Japanese-English translation tasks and showed significant improvement over the baseline system.

Bilingual Continuous-Space Language Model Growing for Statistical Machine Translation

“…However, their method is still slower than the -gram LM. Some other studies try to implement neural network LM or translation model for SMT [27], [28], [29], [30], [31], [32], [33], [34], [35], [36]. But until now, the decoding speed using -gram LM is still the stateof-the-art one.…”