Publication at Faculty of Mathematics and Physics |
2013
Abstract
Current methods for statistical machine translation typically utilize only a limited context in the input sentence. Many language phenomena thus remain out of their reach, for example long-distance agreement in morphologically rich languages or lexical selection often require information from the whole source sentence.
In this work, we present an overview of approaches for including wider context in SMT and describe our first experiments.