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        <title>Deep Learning of Audio and Language Features for Humor Prediction</title>
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    <name type="personal">
        <namePart type="given">Pascale</namePart>
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            <title>Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)</title>
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            <publisher>European Language Resources Association (ELRA)</publisher>
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    <abstract>We propose a comparison between various supervised machine learning methods to predict and detect humor in dialogues. We retrieve our humorous dialogues from a very popular TV sitcom: “The Big Bang Theory”. We build a corpus where punchlines are annotated using the canned laughter embedded in the audio track. Our comparative study involves a linear-chain Conditional Random Field over a Recurrent Neural Network and a Convolutional Neural Network. Using a combination of word-level and audio frame-level features, the CNN outperforms the other methods, obtaining the best F-score of 68.5% over 66.5% by CRF and 52.9% by RNN. Our work is a starting point to developing more effective machine learning and neural network models on the humor prediction task, as well as developing machines capable in understanding humor in general.</abstract>
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%0 Conference Proceedings
%T Deep Learning of Audio and Language Features for Humor Prediction
%A Bertero, Dario
%A Fung, Pascale
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 may
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F bertero-fung-2016-deep
%X We propose a comparison between various supervised machine learning methods to predict and detect humor in dialogues. We retrieve our humorous dialogues from a very popular TV sitcom: “The Big Bang Theory”. We build a corpus where punchlines are annotated using the canned laughter embedded in the audio track. Our comparative study involves a linear-chain Conditional Random Field over a Recurrent Neural Network and a Convolutional Neural Network. Using a combination of word-level and audio frame-level features, the CNN outperforms the other methods, obtaining the best F-score of 68.5% over 66.5% by CRF and 52.9% by RNN. Our work is a starting point to developing more effective machine learning and neural network models on the humor prediction task, as well as developing machines capable in understanding humor in general.
%U https://aclanthology.org/L16-1079
%P 496-501

Markdown (Informal)

[Deep Learning of Audio and Language Features for Humor Prediction](https://aclanthology.org/L16-1079) (Bertero & Fung, LREC 2016)

• Deep Learning of Audio and Language Features for Humor Prediction (Bertero & Fung, LREC 2016)

ACL

• Dario Bertero and Pascale Fung. 2016. Deep Learning of Audio and Language Features for Humor Prediction. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 496–501, Portorož, Slovenia. European Language Resources Association (ELRA).