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    <titleInfo>
        <title>Finding Convincing Arguments Using Scalable Bayesian Preference Learning</title>
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    <name type="personal">
        <namePart type="given">Edwin</namePart>
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    <abstract>We introduce a scalable Bayesian preference learning method for identifying convincing arguments in the absence of gold-standard ratings or rankings. In contrast to previous work, we avoid the need for separate methods to perform quality control on training data, predict rankings and perform pairwise classification. Bayesian approaches are an effective solution when faced with sparse or noisy training data, but have not previously been used to identify convincing arguments. One issue is scalability, which we address by developing a stochastic variational inference method for Gaussian process (GP) preference learning. We show how our method can be applied to predict argument convincingness from crowdsourced data, outperforming the previous state-of-the-art, particularly when trained with small amounts of unreliable data. We demonstrate how the Bayesian approach enables more effective active learning, thereby reducing the amount of data required to identify convincing arguments for new users and domains. While word embeddings are principally used with neural networks, our results show that word embeddings in combination with linguistic features also benefit GPs when predicting argument convincingness.</abstract>
    <identifier type="citekey">simpson-gurevych-2018-finding</identifier>
    <identifier type="doi">10.1162/tacl_a_00026</identifier>
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        <url>https://aclanthology.org/Q18-1026</url>
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    <part>
        <date>2018</date>
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%0 Journal Article
%T Finding Convincing Arguments Using Scalable Bayesian Preference Learning
%A Simpson, Edwin
%A Gurevych, Iryna
%J Transactions of the Association for Computational Linguistics
%D 2018
%V 6
%I MIT Press
%C Cambridge, MA
%F simpson-gurevych-2018-finding
%X We introduce a scalable Bayesian preference learning method for identifying convincing arguments in the absence of gold-standard ratings or rankings. In contrast to previous work, we avoid the need for separate methods to perform quality control on training data, predict rankings and perform pairwise classification. Bayesian approaches are an effective solution when faced with sparse or noisy training data, but have not previously been used to identify convincing arguments. One issue is scalability, which we address by developing a stochastic variational inference method for Gaussian process (GP) preference learning. We show how our method can be applied to predict argument convincingness from crowdsourced data, outperforming the previous state-of-the-art, particularly when trained with small amounts of unreliable data. We demonstrate how the Bayesian approach enables more effective active learning, thereby reducing the amount of data required to identify convincing arguments for new users and domains. While word embeddings are principally used with neural networks, our results show that word embeddings in combination with linguistic features also benefit GPs when predicting argument convincingness.
%R 10.1162/tacl_a_00026
%U https://aclanthology.org/Q18-1026
%U https://doi.org/10.1162/tacl_a_00026
%P 357-371

Markdown (Informal)

[Finding Convincing Arguments Using Scalable Bayesian Preference Learning](https://aclanthology.org/Q18-1026) (Simpson & Gurevych, TACL 2018)

• Finding Convincing Arguments Using Scalable Bayesian Preference Learning (Simpson & Gurevych, TACL 2018)

ACL

• Edwin Simpson and Iryna Gurevych. 2018. Finding Convincing Arguments Using Scalable Bayesian Preference Learning. Transactions of the Association for Computational Linguistics, 6:357–371.