In the framework of the Tc-Star project, we analyze and propose a combination of two Statistical Machine Translation systems: a phrase-based and an N-gram-based one. The exhaustiv...
Dependency analysis of natural language gives rise to non-projective structures. The constraint of well-nestedness on dependency trees has been recently shown to give a good fit ...
We present a method for utilizing unannotated sentences to improve a semantic parser which maps natural language (NL) sentences into their formal meaning representations (MRs). Gi...
This paper describes a new grapheme-tophoneme framework, based on a combination of formal linguistic and statistical methods. A context-free grammar is used to parse words into th...
We present a method for automatic determiner selection, based on an existing language model. We train on the Penn Treebank and also use additional data from the North American New...
This paper presents a three-step dependency parser to parse Chinese deterministically. By dividing a sentence into several parts and parsing them separately, it aims to reduce the...
We present results from a new Interagency Language Roundtable (ILR) based comprehension test. This new test design presents questions at multiple ILR difficulty levels within each...
Douglas Jones, Martha Herzog, Hussny Ibrahim, Arvi...
Vocal activity detection is an important technology for both automatic speech recognition and automatic speech understanding. In meetings, standard vocal activity detection algori...
Collecting supervised training data for automatic speech recognition (ASR) systems is both time consuming and expensive. In this paper we use the notion of virtual evidence in a g...
This paper explores what kind of user simulation model is suitable for developing a training corpus for using Markov Decision Processes (MDPs) to automatically learn dialog strate...