Obtaining high-quality machine translations is still a long way off. A postediting phase is required to improve the output of a machine translation system. An alternative is the s...
When training the parameters for a natural language system, one would prefer to minimize 1-best loss (error) on an evaluation set. Since the error surface for many natural languag...
Thesauri and ontologies provide important value in facilitating access to digital archives by representing underlying principles of organization. Translation of such resources int...
G. Craig Murray, Bonnie J. Dorr, Jimmy J. Lin, Jan...
The LOGON MT demonstrator assembles independently valuable general-purpose NLP components into a machine translation pipeline that capitalizes on output quality. The demonstrator ...
We introduce a semi-supervised approach to training for statistical machine translation that alternates the traditional Expectation Maximization step that is applied on a large tr...
In this paper, we argue that n-gram language models are not sufficient to address word reordering required for Machine Translation. We propose a new distortion model that can be u...
This article describes a machine translation system based on an automatic post-editing strategy: initially translate the input text into the target-language using a rule-based MT ...
The quality of a sentence translated by a machine translation (MT) system is difficult to evaluate. We propose a method for automatically evaluating the quality of each translati...
This paper presents a maximum entropy machine translation system using a minimal set of translation blocks (phrase-pairs). While recent phrase-based statistical machine translatio...
This paper presents MISTRAL, an open source statistical machine translation decoder dedicated to spoken language translation. While typical machine translation systems take a writ...