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OFAI–UKP at HAHA@IberLEF2019: Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning

Miller, Tristan ; Do Dinh, Erik-Lân ; Simpson, Edwin ; Gurevych, Iryna (2019)
OFAI–UKP at HAHA@IberLEF2019: Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning.
Bilbao, Spain
Conference or Workshop Item, Bibliographie

Abstract

Most humour processing systems to date make at best discrete, coarse-grained distinctions between the comical and the conventional, yet such notions are better conceptualized as a broad spectrum. 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 judgments and automatically sourced linguistic annotations. 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 evaluation campaign. We report system performance for the campaign's two subtasks, humour detection and funniness score prediction, and discuss some issues arising from the conversion between the numeric scores used in the HAHA@IberLEF2019 data and the pairwise judgment annotations required for our method.

Item Type: Conference or Workshop Item
Erschienen: 2019
Creators: Miller, Tristan ; Do Dinh, Erik-Lân ; Simpson, Edwin ; Gurevych, Iryna
Type of entry: Bibliographie
Title: OFAI–UKP at HAHA@IberLEF2019: Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning
Language: English
Date: 24 September 2019
Place of Publication: Bilbao, Spain
Book Title: Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2019)
Series: CEUR Workshop Proceedings
Event Location: Bilbao, Spain
URL / URN: http://ceur-ws.org/Vol-2421/HAHA_paper_6.pdf
Corresponding Links:
Abstract:

Most humour processing systems to date make at best discrete, coarse-grained distinctions between the comical and the conventional, yet such notions are better conceptualized as a broad spectrum. 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 judgments and automatically sourced linguistic annotations. 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 evaluation campaign. We report system performance for the campaign's two subtasks, humour detection and funniness score prediction, and discuss some issues arising from the conversion between the numeric scores used in the HAHA@IberLEF2019 data and the pairwise judgment annotations required for our method.

Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
DFG-Graduiertenkollegs
DFG-Graduiertenkollegs > Research Training Group 1994 Adaptive Preparation of Information from Heterogeneous Sources
Date Deposited: 09 Jul 2019 09:57
Last Modified: 24 Jan 2020 12:03
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