In this work, we propose two extensions of standard word lexicons in statistical machine translation: A discriminative word lexicon that uses sentence-level source information to predict the target words and a trigger-based lexicon model that extends IBM model 1 with a second trigger, allowing for a more fine-grained lexical choice of target words. The models capture dependencies that go beyond the scope of conventional SMT models such as phraseand language models. We show that the models improve translation quality by 1% in BLEU over a competitive baseline on a large-scale task.
al. REF suggest another lexical translation approach, named Discriminative Word Lexicon (DWL), concentrating on predicting the presence of target words given the source words.
Extending Statistical Machine Translation with Discriminative and Trigger-Based Lexicon Models
EMNLP
Computer Science