Dialogue act tagging

Xem 1-6 trên 6 kết quả Dialogue act tagging
  • For the task of recognizing dialogue acts, we are applying the Transformation-Based Learning (TBL) machine learning algorithm. To circumvent a sparse data problem, we extract values of well-motivated features of utterances, such as speaker direction, punctuation marks, and a new feature, called dialogue act cues, which we find to be more effective than cue phrases and word n-grams in practice.

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  • Instant Messaging chat sessions are realtime text-based conversations which can be analyzed using dialogue-act models. We describe a statistical approach for modelling and detecting dialogue acts in Instant Messaging dialogue. This involved the collection of a small set of task-based dialogues and annotating them with a revised tag set. We then dealt with segmentation and synchronisation issues which do not arise in spoken dialogue. The model we developed combines naive Bayes and dialogue-act n-grams to obtain better than 80% accuracy in our tagging experiment. ...

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  • Discourse chunking is a simple way to segment dialogues according to how dialogue participants raise topics and negotiate them. This paper explains a method for arranging dialogues into chunks, and also shows how discourse chunking can be used to improve performance for a dialogue act tagger that uses a case-based reasoning approach. applied to the DA tagging task. Their use amounts to a separate tagging task of its own, with the concomitant time-consuming corpus annotation.

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  • This paper describes the application of the PARADISE evaluation framework to the corpus of 662 human-computer dialogues collected in the June 2000 Darpa Communicator data collection. We describe results based on the standard logfile metrics as well as results based on additional qualitative metrics derived using the DATE dialogue act tagging scheme. We show that performance models derived via using the standard metrics can account for 37% of the variance in user satisfaction, and that the addition of DATE metrics improved the models by an absolute 5%. ...

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  • We propose a statistical dialogue analysis model to determine discourse structures as well as speech acts using maximum entropy model. The model can automatically acquire probabilistic discourse knowledge from a discourse tagged corpus to resolve ambiguities. We propose the idea of tagging discourse segment boundaries to represent the structural information of discourse. Using this representation we can effectively combine speech act analysis and discourse structure analysis in one framework.

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  • This paper describes a new efficient speech act type tagging system. This system covers the tasks of (1) segmenting a turn into the optimal number of speech act units (SA units), and (2) assigning a speech act type tag (SA tag) to each SA unit. Our method is based on a theoretically clear statistical model that integrates linguistic, acoustic and situational information. We report tagging experiments on Japanese and English dialogue corpora manually labeled with SA tags.

    pdf8p bunrieu_1 18-04-2013 31 2   Download



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