We show how the integration of an extended lexicon model into the decoder can improve translation performance. The model is based on lexical triggers that capture long-distance dependencies on the sentence level. The results are compared to variants of the model that are applied in reranking of n-best lists. We present how a combined application of these models in search and rescoring gives promising results. Experiments are reported on the GALE Chinese-English task with improvements of up to +0.9% BLEU and -1.5% TER absolute on a competitive baseline.
A comparison of a variant of the trigger-based lexicon model applied in decoding and n-best list reranking can be found in REF .
Comparison of Extended Lexicon Models in Search and Rescoring for SMT
HLT-NAACL, Companion Volume: Short Papers
Computer Science