Paper: Fast and Robust Neural Network Joint Models for Statistical Machine Translation

ACL ID P14-1129
Title Fast and Robust Neural Network Joint Models for Statistical Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Recent work has shown success in us- ing neural network language models (NNLMs) as features in MT systems. Here, we present a novel formulation for a neural network joint model (NNJM), which augments the NNLM with a source context window. Our model is purely lexi- calized and can be integrated into any MT decoder. We also present several varia- tions of the NNJM which provide signif- icant additive improvements. Although the model is quite simple, it yields strong empirical results. On the NIST OpenMT12 Arabic-English condi- tion, the NNJM features produce a gain of +3.0 BLEU on top of a powerful, feature- rich baseline which already includes a target-only NNLM. The NNJM features also produce a gain of +6.3 BLEU on top of a simpler baseline equivalent to Chi- ang?s (2007) original Hiero im...