2015/06/01 by Amir Aghasadeghi, Amir Pouya Aghasadeghi, Aghasadeghi, Amir Pouya +2
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Speech and dialogue systems #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.1506.00406
openalex publication_date 2015/06/01 · arxiv created 2016/05/24 · arxiv updated 2016/05/25 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
In this paper, we propose two new features for estimating phrase-based machine translation parameters from mainly monolingual data. Our method is based on two recently introduced neural network vector representation models for words and sentences. It is the first time that these models have been used in an end to end phrase-based machine translation system. Scores obtained from our method can recover more than 80% of BLEU loss caused by removing phrase table probabilities. We also show that our features combined with the phrase table probabilities improve the BLEU score by absolute 0.74 points.