The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Este artigo discute o processo de compreensão do discurso em sistemas de diálogo falado. Este processo permite que um sistema compreenda as declarações do usuário a partir do contexto de um diálogo. A ambigüidade nas declarações do usuário causada por múltiplas hipóteses de reconhecimento de fala e resultados de análise às vezes torna difícil para um sistema decidir sobre uma única interpretação da intenção do usuário. Como solução, foi proposta a ideia de reter interpretações possíveis como múltiplos estados de diálogo e resolver a ambiguidade usando declarações de usuário sucessivas. Embora esta abordagem tenha provado melhorar a precisão da compreensão do discurso, são necessárias regras cuidadosamente criadas à mão para classificar com precisão os estados do diálogo. Este artigo propõe classificar automaticamente vários estados de diálogo usando informações estatísticas obtidas de corpora de diálogo. Os resultados experimentais nos domínios de reserva de passagens de trem e serviço de informações meteorológicas mostram que as informações estatísticas podem melhorar significativamente a precisão da classificação dos estados de diálogo, bem como a precisão dos slots e a taxa de erro de conceito dos estados de diálogo mais bem classificados.
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Ryuichiro HIGASHINAKA, Mikio NAKANO, "Ranking Multiple Dialogue States by Corpus Statistics to Improve Discourse Understanding in Spoken Dialogue Systems" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 9, pp. 1771-1782, September 2009, doi: 10.1587/transinf.E92.D.1771.
Abstract: This paper discusses the discourse understanding process in spoken dialogue systems. This process enables a system to understand user utterances from the context of a dialogue. Ambiguity in user utterances caused by multiple speech recognition hypotheses and parsing results sometimes makes it difficult for a system to decide on a single interpretation of a user intention. As a solution, the idea of retaining possible interpretations as multiple dialogue states and resolving the ambiguity using succeeding user utterances has been proposed. Although this approach has proven to improve discourse understanding accuracy, carefully created hand-crafted rules are necessary in order to accurately rank the dialogue states. This paper proposes automatically ranking multiple dialogue states using statistical information obtained from dialogue corpora. The experimental results in the train ticket reservation and weather information service domains show that the statistical information can significantly improve the ranking accuracy of dialogue states as well as the slot accuracy and the concept error rate of the top-ranked dialogue states.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.1771/_p
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@ARTICLE{e92-d_9_1771,
author={Ryuichiro HIGASHINAKA, Mikio NAKANO, },
journal={IEICE TRANSACTIONS on Information},
title={Ranking Multiple Dialogue States by Corpus Statistics to Improve Discourse Understanding in Spoken Dialogue Systems},
year={2009},
volume={E92-D},
number={9},
pages={1771-1782},
abstract={This paper discusses the discourse understanding process in spoken dialogue systems. This process enables a system to understand user utterances from the context of a dialogue. Ambiguity in user utterances caused by multiple speech recognition hypotheses and parsing results sometimes makes it difficult for a system to decide on a single interpretation of a user intention. As a solution, the idea of retaining possible interpretations as multiple dialogue states and resolving the ambiguity using succeeding user utterances has been proposed. Although this approach has proven to improve discourse understanding accuracy, carefully created hand-crafted rules are necessary in order to accurately rank the dialogue states. This paper proposes automatically ranking multiple dialogue states using statistical information obtained from dialogue corpora. The experimental results in the train ticket reservation and weather information service domains show that the statistical information can significantly improve the ranking accuracy of dialogue states as well as the slot accuracy and the concept error rate of the top-ranked dialogue states.},
keywords={},
doi={10.1587/transinf.E92.D.1771},
ISSN={1745-1361},
month={September},}
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TY - JOUR
TI - Ranking Multiple Dialogue States by Corpus Statistics to Improve Discourse Understanding in Spoken Dialogue Systems
T2 - IEICE TRANSACTIONS on Information
SP - 1771
EP - 1782
AU - Ryuichiro HIGASHINAKA
AU - Mikio NAKANO
PY - 2009
DO - 10.1587/transinf.E92.D.1771
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2009
AB - This paper discusses the discourse understanding process in spoken dialogue systems. This process enables a system to understand user utterances from the context of a dialogue. Ambiguity in user utterances caused by multiple speech recognition hypotheses and parsing results sometimes makes it difficult for a system to decide on a single interpretation of a user intention. As a solution, the idea of retaining possible interpretations as multiple dialogue states and resolving the ambiguity using succeeding user utterances has been proposed. Although this approach has proven to improve discourse understanding accuracy, carefully created hand-crafted rules are necessary in order to accurately rank the dialogue states. This paper proposes automatically ranking multiple dialogue states using statistical information obtained from dialogue corpora. The experimental results in the train ticket reservation and weather information service domains show that the statistical information can significantly improve the ranking accuracy of dialogue states as well as the slot accuracy and the concept error rate of the top-ranked dialogue states.
ER -