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    » Predicting Success in Dialogue
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    ACL
    2009
    91views Computational Linguistics» more  ACL 2009»
    Predicting Barge-in Utterance Errors by using Implicitly-Supervised ASR Accuracy and Barge-in Rate per User
    13 years 7 months ago
    Predicting Barge-in Utterance Errors by using Implicitly-Supervised ASR Accuracy and Barge-in Rate per User
    Download  sslab.nuee.nagoya-u.ac.jp
    Modeling of individual users is a promising way of improving the performance of spoken dialogue systems deployed for the general public and utilized repeatedly. We define "im...
    Kazunori Komatani, Alexander I. Rudnicky
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    ACL
    2006
    110views Computational Linguistics» more  ACL 2006»
    Learning to Say It Well: Reranking Realizations by Predicted Synthesis Quality
    13 years 11 months ago
    Learning to Say It Well: Reranking Realizations by Predicted Synthesis Quality
    Download  www.ling.ohio-state.edu
    This paper presents a method for adapting a language generator to the strengths and weaknesses of a synthetic voice, thereby improving the naturalness of synthetic speech in a spo...
    Crystal Nakatsu, Michael White
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    ACL
    2009
    146views Computational Linguistics» more  ACL 2009»
    Automatic Generation of Information-seeking Questions Using Concept Clusters
    13 years 7 months ago
    Automatic Generation of Information-seeking Questions Using Concept Clusters
    Download  www.aclweb.org
    One of the basic problems of efficiently generating information-seeking dialogue in interactive question answering is to find the topic of an information-seeking question with res...
    Shuguang Li, Suresh Manandhar
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    ICDE
    2012
    IEEE
    304views Database» more  ICDE 2012»
    Learning-based Query Performance Modeling and Prediction
    12 years 8 days ago
    Learning-based Query Performance Modeling and Prediction
    Download  www.cs.brown.edu
    — Accurate query performance prediction (QPP) is central to effective resource management, query optimization and query scheduling. Analytical cost models, used in current genera...
    Mert Akdere, Ugur Çetintemel, Matteo Rionda...
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    WSDM
    2012
    ACM
    283views Data Mining» more  WSDM 2012»
    The life and death of online groups: predicting group growth and longevity
    12 years 5 months ago
    The life and death of online groups: predicting group growth and longevity
    Download  cs.stanford.edu
    We pose a fundamental question in understanding how to identify and design successful communities: What factors predict whether a community will grow and survive in the long term?...
    Sanjay Ram Kairam, Dan J. Wang, Jure Leskovec
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