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        <title>Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics</title>
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    <name type="personal">
        <namePart type="given">Martin</namePart>
        <namePart type="family">Boyanov</namePart>
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    <name type="personal">
        <namePart type="given">Preslav</namePart>
        <namePart type="family">Nakov</namePart>
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    <name type="personal">
        <namePart type="given">Alessandro</namePart>
        <namePart type="family">Moschitti</namePart>
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    <name type="personal">
        <namePart type="given">Giovanni</namePart>
        <namePart type="family">Da San Martino</namePart>
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    <name type="personal">
        <namePart type="given">Ivan</namePart>
        <namePart type="family">Koychev</namePart>
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        <dateIssued>2017-09</dateIssued>
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            <title>Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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            <publisher>INCOMA Ltd.</publisher>
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    <abstract>We propose to use question answering (QA) data from Web forums to train chat-bots from scratch, i.e., without dialog data. First, we extract pairs of question and answer sentences from the typically much longer texts of questions and answers in a forum. We then use these shorter texts to train seq2seq models in a more efficient way. We further improve the parameter optimization using a new model selection strategy based on QA measures. Finally, we propose to use extrinsic evaluation with respect to a QA task as an automatic evaluation method for chatbot systems. The evaluation shows that the model achieves a MAP of 63.5% on the extrinsic task. Moreover, our manual evaluation demonstrates that the model can answer correctly 49.5% of the questions when they are similar in style to how questions are asked in the forum, and 47.3% of the questions, when they are more conversational in style.</abstract>
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    <identifier type="doi">10.26615/978-954-452-049-6_018</identifier>
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        <date>2017-09</date>
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            <start>121</start>
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%0 Conference Proceedings
%T Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics
%A Boyanov, Martin
%A Nakov, Preslav
%A Moschitti, Alessandro
%A Da San Martino, Giovanni
%A Koychev, Ivan
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F boyanov-etal-2017-building
%X We propose to use question answering (QA) data from Web forums to train chat-bots from scratch, i.e., without dialog data. First, we extract pairs of question and answer sentences from the typically much longer texts of questions and answers in a forum. We then use these shorter texts to train seq2seq models in a more efficient way. We further improve the parameter optimization using a new model selection strategy based on QA measures. Finally, we propose to use extrinsic evaluation with respect to a QA task as an automatic evaluation method for chatbot systems. The evaluation shows that the model achieves a MAP of 63.5% on the extrinsic task. Moreover, our manual evaluation demonstrates that the model can answer correctly 49.5% of the questions when they are similar in style to how questions are asked in the forum, and 47.3% of the questions, when they are more conversational in style.
%R 10.26615/978-954-452-049-6_018
%U https://doi.org/10.26615/978-954-452-049-6_018
%P 121-129

Markdown (Informal)

[Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics](https://doi.org/10.26615/978-954-452-049-6_018) (Boyanov et al., RANLP 2017)

• Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics (Boyanov et al., RANLP 2017)

ACL

• Martin Boyanov, Preslav Nakov, Alessandro Moschitti, Giovanni Da San Martino, and Ivan Koychev. 2017. Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 121–129, Varna, Bulgaria. INCOMA Ltd..