A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
OFAI-UKP at HAHA@IberLEF2019: Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning
[article]
2020
arXiv
pre-print
Preserved Fulltext
Other Versions
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
2021
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
unpublished
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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