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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 address the problem of dynamically modeling and adapting to unknown users in resource-scarce domains in the context of interactive spoken dialogue systems. As an example, we show how a system can l...
We propose a method for learning dialogue management policies from a fixed data set. The method addresses the challenges posed by Information State Update (ISU)-based dialogue systems, which r...
In this article we use well-known machine learning methods to tackle a novel task, namely the classification of non-sentential utterances (NSUs) in dialogue. We introduce a fine-grained ...
This article focuses on the analysis and prediction of corrections, defined as turns where a user tries to correct a prior error made by a spoken dialogue system. We describe our labeling procedure of...
Building Coherence and Cohesion by Maite Taboada presents a detailed account olected aspects of the discourse of scheduling dialogs, which are conversations between two people trying to arrange a mee...
Agreement statistics play an important role in the evaluation of coding schemes for discourse and dialogue. Unfortunately there is a lack of understanding regarding appropriate agreement measures and ...
The problem of pragmatics arises as soon as we move beyond the linguistic analysis of an utterance and ask what the speaker meant by it. Now, speaker’s meaning is a particular case of intention, and a...
We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as STATEMENT, Question, BACKCHANNEL, Agreement, Disagreement, and Apology. Our ...

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