Paper: Learning Continuous Phrase Representations for Translation Modeling

ACL ID P14-1066
Title Learning Continuous Phrase Representations for Translation Modeling
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

This paper tackles the sparsity problem in estimating phrase translation probabilities by learning continuous phrase representa- tions, whose distributed nature enables the sharing of related phrases in their represen- tations. A pair of source and target phrases are projected into continuous-valued vec- tor representations in a low-dimensional latent space, where their translation score is computed by the distance between the pair in this new space. The projection is performed by a neural network whose weights are learned on parallel training data. Experimental evaluation has been performed on two WMT translation tasks. Our best result improves the performance of a state-of-the-art phrase-based statistical machine translation system trained on WMT 2012 French-English data b...