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OFAI-UKP at HAHA@IberLEF2019: Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning [article]

Tristan Miller, Erik-Lân Do Dinh, Edwin Simpson, Iryna Gurevych
2020 arXiv   pre-print
In this paper, we present a probabilistic approach, a variant of Gaussian process preference learning (GPPL), that learns to rank and rate the humorousness of short texts by exploiting human preference  ...  We apply our system, which had previously shown good performance on English-language one-liners annotated with pairwise humorousness annotations, to the Spanish-language data set of the HAHA@IberLEF2019  ...  This paper describes the OFAI-UKP system that participated in both subtasks of the HAHA@IberLEF2019 evaluation campaign: binary classification of tweets as humorous or not humorous, and the quantification  ... 
arXiv:2008.00853v1 fatcat:gtp5k2pejzcudemo5ryisivqni

"So You Think You're Funny?": Rating the Humour Quotient in Standup Comedy

Anirudh Mittal, Pranav Jeevan P, Prerak Gandhi, Diptesh Kanojia, Pushpak Bhattacharyya
2021 Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing   unpublished
OFAI-UKP at Graves, Alex and Fernández, Santiago and Schmid- HAHA@IberLEF2019: Predicting the Humorous- huber, Jürgen. 2005.  ...  Bidirectional lstm networks ness of Tweets Using Gaussian Process Preference for improved phoneme classification and recogni- Learning. tion. pages 799–804. Jeff Green. 2018.  ... 
doi:10.18653/v1/2021.emnlp-main.789 fatcat:l2cfkqkblbc6tf7kf4g2ka6lfy