Meta-level Statistical Machine Translation
Sajad Ebrahimi, Kourosh Meshgi, Shahram Khadivi, Mohammad Ebrahim Shiri · International Joint Conference on Natural Language Processing · 2013
We propose a simple and effective method to build a meta-level Statistical Machine Translation (SMT), called meta-SMT, for system combination. Our approach is based on the framework of Stacked Generalization, also known as Stacking, which is an ensemble learning algorithm, widely used in machine learning tasks. First, a collection of base-level SMTs is generated for obtaining a meta-level corpus. Then a meta-level SMT is trained on this corpus. In this paper we address the issue of how to adapt stacked generalization to SMT. We evaluate our approach on Englishto-Persian machine translation. Experimental results show that our approach leads to significant improvements in translation quality over a phrase-based baseline by about 1.1 BLEU points.