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We present recent work in the area of Dialogue Act (DA) tagging. Identifying the dialogue acts of utterances is recognised as an important step towards understanding the content and nature of what speakers say. Our experiments investigate the use of a simple dialogue act classifier based on purely intra-utterance features — principally involving word n-gram(More)
Within the EU-funded COMPANIONS project, we are working to evaluate new collaborative conversational models of dialogue. Such an evaluation requires us to benchmark approaches to companionable dialogue. In order to determine the impact of system strategies on our evaluation paradigm, we need to generate a range of companionable conversations, using dialogue(More)
We present a natural-language customer service application for a telephone banking call center, developed as part of the AMITIES dialogue project (Automated Multilingual Interaction with Information and Services). Our dialogue system, based on empirical data gathered from real call-center conversations, features data-driven techniques that allow for spoken(More)
In this paper, we present an investigation into the use of cue phrases as a basis for dialogue act classification. We define what we mean by cue phrases, and describe how we extract them from a manually labelled corpus of dialogue. We describe one method of evaluating the usefulness of such cue phrases, by applying them directly as a classifier to unseen(More)
For the AMITIÉS multilingual human-computer dialogue project [1], we have developed new methods for the manual annotation of spoken dialogue transcriptions from European financial call centers on multiple levels. We have modified the DAMSL schema [2] to create a dialogue act taxon-omy appropriate to the functions of call center dialogues. We use a(More)
We present a prototype natural-language problem-solving application for a financial services call center, developed as part of the Amitiés multilingual human-computer dialogue project. Our automated dialogue system, based on empirical evidence from real call-center conversations, features a data-driven approach that allows for mixed system/customer(More)
We present recent work in the area of Cross-Domain Dialogue Act tagging. Our experiments investigate the use of a simple dialogue act classifier based on purely intra-utterance features-principally involving word n-gram cue phrases. We apply automatically extracted cues from one corpus to a new annotated data set, to determine the portability and generality(More)