Shallow Semantically-Informed PBSMT and HPBSMT

This paper describes shallow semantically-informed Hierarchical Phrase-based SMT (HPBSMT) and Phrase-Based SMT (PBSMT) systems developed at Dublin City University for participation in the translation task between EN-ES and ES-EN at the Workshop on Statistical Machine Translation (WMT 13). The system uses PBSMT and HPBSMT decoders with multiple LMs, but will run only one decoding path decided before starting translation. Therefore the paper does not present a multi-engine system combination. We investigate three types of shallow semantics: (i) Quality Estimation (QE) score, (ii) genre ID, and (iii) context ID derived from context-dependent language models. Our results show that the improvement is 0.8 points absolute (BLEU) for EN-ES and 0.7 points for ES-EN compared to the standard PBSMT system (single best system). It is important to note that we developed this method when the standard (confusion network-based) system combination is ineffective such as in the case when the input is only two.