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Language comprehension involves the simultaneous processing of information at the phonological, syntactic, and lexical level. We track these three distinct streams of information in the brain by using...
Most previous work on trainable language generation has focused on two paradigms: (a) using a generation decisions of an existing generator. Both approaches rely on the existence of a handcrafted gene...
We investigate the differences between language models compiled from original target-language texts and those compiled from texts manually translated to the target language. Corroborating established ...
This article investigates the effects of different degrees of contextual granularity on language model performance. It presents a new language model that combines clustering and half-contextualization...
We show that under certain conditions, a language model can be trained on the basis of a second language model. The main instance of the technique trains a finite automaton on the basis of a p...
Incremental Language Models for Speech Recognition using Finite-State Transducers.
Automatic Induction of N-Gram Language Models from a Natural Language Grammar.
Using Untranscribed User Utterances for Improving Language Models based on Confidence Scoring.
Generalized Linear Interpolation of Language Models.
A Collective Data Generation Method for Speech Language Models.
Recently we began using Amazon Mechanical Turk (AMT), an Internet marketplace, to deploy our spoken dialogue systems to large audiences for user testing and data collection purposes. This crowdsourcin...

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