Simon Carter


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Exact Sampling and Decoding in High-Order Hidden Markov Models
Simon Carter | Marc Dymetman | Guillaume Bouchard
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Optimization and Sampling for NLP from a Unified Viewpoint
Marc Dymetman | Guillaume Bouchard | Simon Carter
Proceedings of the First International Workshop on Optimization Techniques for Human Language Technology


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Discriminative Syntactic Reranking for Statistical Machine Translation
Simon Carter | Christof Monz
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers

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.


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TheQMUL system description for IWSLT 2008.
Simon Carter | Christof Monz | Sirvan Yahyaei
Proceedings of the 5th International Workshop on Spoken Language Translation: Evaluation Campaign

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.