Paper: Stacking for Statistical Machine Translation

ACL ID P13-2060
Title Stacking for Statistical Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2013

We propose the use of stacking, an ensem- ble learning technique, to the statistical machine translation (SMT) models. A diverse ensem- ble of weak learners is created using the same SMT engine (a hierarchical phrase-based sys- tem) by manipulating the training data and a strong model is created by combining the weak models on-the-fly. Experimental results on two language pairs and three different sizes of train- ing data show significant improvements of up to 4 BLEU points over a conventionally trained SMT model.