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        <title>Predicting Causes of Reformulation in Intelligent Assistants</title>
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        <namePart type="given">Shumpei</namePart>
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            <title>Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue</title>
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        <name type="personal">
            <namePart type="given">Kristiina</namePart>
            <namePart type="family">Jokinen</namePart>
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            <namePart type="given">Manfred</namePart>
            <namePart type="family">Stede</namePart>
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    <abstract>Intelligent assistants (IAs) such as Siri and Cortana conversationally interact with users and execute a wide range of actions (e.g., searching the Web, setting alarms, and chatting). IAs can support these actions through the combination of various components such as automatic speech recognition, natural language understanding, and language generation. However, the complexity of these components hinders developers from determining which component causes an error. To remove this hindrance, we focus on reformulation, which is a useful signal of user dissatisfaction, and propose a method to predict the reformulation causes. We evaluate the method using the user logs of a commercial IA. The experimental results have demonstrated that features designed to detect the error of a specific component improve the performance of reformulation cause detection.</abstract>
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%0 Conference Proceedings
%T Predicting Causes of Reformulation in Intelligent Assistants
%A Sano, Shumpei
%A Kaji, Nobuhiro
%A Sassano, Manabu
%Y Jokinen, Kristiina
%Y Stede, Manfred
%Y DeVault, David
%Y Louis, Annie
%S Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue
%D 2017
%8 August
%I Association for Computational Linguistics
%C Saarbrücken, Germany
%F sano-etal-2017-predicting
%X Intelligent assistants (IAs) such as Siri and Cortana conversationally interact with users and execute a wide range of actions (e.g., searching the Web, setting alarms, and chatting). IAs can support these actions through the combination of various components such as automatic speech recognition, natural language understanding, and language generation. However, the complexity of these components hinders developers from determining which component causes an error. To remove this hindrance, we focus on reformulation, which is a useful signal of user dissatisfaction, and propose a method to predict the reformulation causes. We evaluate the method using the user logs of a commercial IA. The experimental results have demonstrated that features designed to detect the error of a specific component improve the performance of reformulation cause detection.
%R 10.18653/v1/W17-5536
%U https://aclanthology.org/W17-5536
%U https://doi.org/10.18653/v1/W17-5536
%P 299-309

Markdown (Informal)

[Predicting Causes of Reformulation in Intelligent Assistants](https://aclanthology.org/W17-5536) (Sano et al., SIGDIAL 2017)

• Predicting Causes of Reformulation in Intelligent Assistants (Sano et al., SIGDIAL 2017)

ACL

• Shumpei Sano, Nobuhiro Kaji, and Manabu Sassano. 2017. Predicting Causes of Reformulation in Intelligent Assistants. In Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, pages 299–309, Saarbrücken, Germany. Association for Computational Linguistics.