@inproceedings{boyanov-etal-2017-building,
    title = "Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics",
    author = "Boyanov, Martin  and
      Nakov, Preslav  and
      Moschitti, Alessandro  and
      Da San Martino, Giovanni  and
      Koychev, Ivan",
    editor = "Mitkov, Ruslan  and
      Angelova, Galia",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://aclanthology.org/R17-1018/",
    doi = "10.26615/978-954-452-049-6_018",
    pages = "121--129",
    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."
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        <title>Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics</title>
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        <namePart type="given">Martin</namePart>
        <namePart type="family">Boyanov</namePart>
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        <namePart type="given">Preslav</namePart>
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        <namePart type="given">Alessandro</namePart>
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    <name type="personal">
        <namePart type="given">Ivan</namePart>
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        <dateIssued>2017-09</dateIssued>
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            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
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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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        <url>https://aclanthology.org/R17-1018/</url>
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    <part>
        <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://aclanthology.org/R17-1018/
%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://aclanthology.org/R17-1018/) (Boyanov et al., RANLP 2017)
ACL