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        <title>Improving coreference resolution with automatically predicted prosodic information</title>
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        <namePart type="given">Ina</namePart>
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        <namePart type="given">Sabrina</namePart>
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    <name type="personal">
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            <title>Proceedings of the Workshop on Speech-Centric Natural Language Processing</title>
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            <namePart type="given">Srinivas</namePart>
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    <abstract>Adding manually annotated prosodic information, specifically pitch accents and phrasing, to the typical text-based feature set for coreference resolution has previously been shown to have a positive effect on German data. Practical applications on spoken language, however, would rely on automatically predicted prosodic information. In this paper we predict pitch accents (and phrase boundaries) using a convolutional neural network (CNN) model from acoustic features extracted from the speech signal. After an assessment of the quality of these automatic prosodic annotations, we show that they also significantly improve coreference resolution.</abstract>
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%0 Conference Proceedings
%T Improving coreference resolution with automatically predicted prosodic information
%A Roesiger, Ina
%A Stehwien, Sabrina
%A Riester, Arndt
%A Vu, Ngoc Thang
%Y Ruiz, Nicholas
%Y Bangalore, Srinivas
%S Proceedings of the Workshop on Speech-Centric Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F roesiger-etal-2017-improving
%X Adding manually annotated prosodic information, specifically pitch accents and phrasing, to the typical text-based feature set for coreference resolution has previously been shown to have a positive effect on German data. Practical applications on spoken language, however, would rely on automatically predicted prosodic information. In this paper we predict pitch accents (and phrase boundaries) using a convolutional neural network (CNN) model from acoustic features extracted from the speech signal. After an assessment of the quality of these automatic prosodic annotations, we show that they also significantly improve coreference resolution.
%R 10.18653/v1/W17-4610
%U https://aclanthology.org/W17-4610
%U https://doi.org/10.18653/v1/W17-4610
%P 78-83

Markdown (Informal)

[Improving coreference resolution with automatically predicted prosodic information](https://aclanthology.org/W17-4610) (Roesiger et al., 2017)

• Improving coreference resolution with automatically predicted prosodic information (Roesiger et al., 2017)

ACL

• Ina Roesiger, Sabrina Stehwien, Arndt Riester, and Ngoc Thang Vu. 2017. Improving coreference resolution with automatically predicted prosodic information. In Proceedings of the Workshop on Speech-Centric Natural Language Processing, pages 78–83, Copenhagen, Denmark. Association for Computational Linguistics.