A vector-space dynamic feature for phrase-based statistical machine translation

Abstract

In this paper, we propose and evaluate a novel dynamic feature function for log-linear model combinations in phrase-based statistical machine translation. The feature function is inspired on the popularly known vector-space model which is typically used in information retrieval and text mining applications, and it aims at improving translation unit selection at decoding time by incorporating context information from the source language. Significant improvements on an English-Spanish experimental corpus are presented and discussed.