Publishing Partner: Cambridge University Press CUP Extra Publisher Login
amazon logo
More Info


New from Oxford University Press!

ad

Speaking American: A History of English in the United States

By Richard W. Bailey

"Takes a novel approach to the history of American English by focusing on hotbeds of linguistic activity throughout American history."


New from Cambridge University Press!

ad

Language, Literacy, and Technology

By Richard Kern

"In this book, Richard Kern explores how technology matters to language and the ways in which we use it. Kern reveals how material, social and individual resources interact in the design of textual meaning, and how that interaction plays out across contexts of communication, different situations of technological mediation, and different moments in time."


Academic Paper


Title: Estimating the number of segments for improving dialogue act labelling
Author: Vicent Tamarit
Institution: Universidad Politécnica de Valencia
Author: Carlos-D. Martínez-Hinarejos
Institution: Universidad Politécnica de Valencia
Author: José-Miguel Benedí
Institution: Universidad Politécnica de Valencia
Linguistic Field: Computational Linguistics; Text/Corpus Linguistics
Abstract: In dialogue systems it is important to label the dialogue turns with dialogue-related meaning. Each turn is usually divided into segments and these segments are labelled with dialogue acts (DAs). A DA is a representation of the functional role of the segment. Each segment is labelled with one DA, representing its role in the ongoing discourse. The sequence of DAs given a dialogue turn is used by the dialogue manager to understand the turn. Probabilistic models that perform DA labelling can be used on segmented or unsegmented turns. The last option is more likely for a practical dialogue system, but it provides poorer results. In that case, a hypothesis for the number of segments can be provided to improve the results. We propose some methods to estimate the probability of the number of segments based on the transcription of the turn. The new labelling model includes the estimation of the probability of the number of segments in the turn. We tested this new approach with two different dialogue corpora: Switchboard and Dihana . The results show that this inclusion significantly improves the labelling accuracy.

CUP AT LINGUIST

This article appears IN Natural Language Engineering Vol. 18, Issue 1, which you can READ on Cambridge's site .



Add a new paper
Return to Academic Papers main page
Return to Directory of Linguists main page