Smaller, faster and accurate models for statistical machine translation
Ashish Vaswani · University of Southern California Digital Library · 2014
The goal of machine translation is to translate from one natural language into another using computers. The current dominant approach to machine translation, statistical machine translation (SMT), uses large amounts of training data to automatically learn to translate. SMT systems typically contain three primary components: word alignment models, translation rules, and language models. These are some of the largest models in all of natural language processing, containing up to a billion parameters. Learning and employing these components pose difficult challenges of scale and generalization: using large models in statistical machine translation can slow down the translation process