This paper describes a method that successfully exploits simple syntactic features for n-best translation candidate reranking using perceptrons. Our approach uses discriminative language modelling to rerank the n-best translations generated by a statistical machine translation system. The performance is evaluated for Arabic-to-English translation using NIST’s MT-Eval benchmarks. Whilst parse trees do not consistently help, we show how features extracted from a simple Part-of-Speech annotation layer outperform two competitive baselines, leading to significant BLEU improvements on three different test sets.

The QMUL system to the IWSLT 2008 evaluation campaign is a phrase-based statistical MT system implemented in C++. The decoder employs a multi-stack architecture, and uses a beam to manage the search space. We participated in both BTEC Arabic → English and Chinese → English tracks, as well as the PIVOT task. In our first submission to IWSLT, we are particularly interested in seeing how our SMT system performs with speech input, having so far only worked with and translated newswire data sets.