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    <titleInfo>
        <title>Performance of BERT on Persuasion for Good</title>
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    <name type="personal">
        <namePart type="given">Saumajit</namePart>
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    <name type="personal">
        <namePart type="given">Kanika</namePart>
        <namePart type="family">Kalra</namePart>
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    <name type="personal">
        <namePart type="given">Manasi</namePart>
        <namePart type="family">Patwardhan</namePart>
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    <name type="personal">
        <namePart type="given">Shirish</namePart>
        <namePart type="family">Karande</namePart>
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            <title>Proceedings of the 18th International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="given">Sivaji</namePart>
            <namePart type="family">Bandyopadhyay</namePart>
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            <namePart type="given">Sobha</namePart>
            <namePart type="given">Lalitha</namePart>
            <namePart type="family">Devi</namePart>
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            <namePart type="given">Pushpak</namePart>
            <namePart type="family">Bhattacharyya</namePart>
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            <publisher>NLP Association of India (NLPAI)</publisher>
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    <abstract>We consider the task of automatically classifying the persuasion strategy employed by an utterance in a dialog. We base our work on the PERSUASION-FOR-GOOD dataset, which is composed of conversations between crowdworkers trying to convince each other to make donations to a charity. Currently, the best known performance on this dataset, for classification of persuader’s strategy, is not derived by employing pretrained language models like BERT. We observe that a straightforward fine-tuning of BERT does not provide significant performance gain. Nevertheless, nonuniformly sampling to account for the class imbalance and a cost function enforcing a hierarchical probabilistic structure on the classes provides an absolute improvement of 10.79% F1 over the previously reported results. On the same dataset, we replicate the framework for classifying the persuadee’s response.</abstract>
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        <date>2021-12</date>
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            <start>313</start>
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%0 Conference Proceedings
%T Performance of BERT on Persuasion for Good
%A Saha, Saumajit
%A Kalra, Kanika
%A Patwardhan, Manasi
%A Karande, Shirish
%Y Bandyopadhyay, Sivaji
%Y Devi, Sobha Lalitha
%Y Bhattacharyya, Pushpak
%S Proceedings of the 18th International Conference on Natural Language Processing (ICON)
%D 2021
%8 December
%I NLP Association of India (NLPAI)
%C National Institute of Technology Silchar, Silchar, India
%F saha-etal-2021-performance
%X We consider the task of automatically classifying the persuasion strategy employed by an utterance in a dialog. We base our work on the PERSUASION-FOR-GOOD dataset, which is composed of conversations between crowdworkers trying to convince each other to make donations to a charity. Currently, the best known performance on this dataset, for classification of persuader’s strategy, is not derived by employing pretrained language models like BERT. We observe that a straightforward fine-tuning of BERT does not provide significant performance gain. Nevertheless, nonuniformly sampling to account for the class imbalance and a cost function enforcing a hierarchical probabilistic structure on the classes provides an absolute improvement of 10.79% F1 over the previously reported results. On the same dataset, we replicate the framework for classifying the persuadee’s response.
%U https://aclanthology.org/2021.icon-main.38
%P 313-323

Markdown (Informal)

[Performance of BERT on Persuasion for Good](https://aclanthology.org/2021.icon-main.38) (Saha et al., ICON 2021)

• Performance of BERT on Persuasion for Good (Saha et al., ICON 2021)

ACL

• Saumajit Saha, Kanika Kalra, Manasi Patwardhan, and Shirish Karande. 2021. Performance of BERT on Persuasion for Good. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 313–323, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).