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
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Miller, Tristan ; Do Dinh, Erik-Lân ; Simpson, Edwin ; Gurevych, Iryna |
| Art des Eintrags: | Bibliographie |
| Titel: | OFAI–UKP at HAHA@IberLEF2019: Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning |
| Sprache: | Englisch |
| Publikationsjahr: | 24 September 2019 |
| Ort: | Bilbao, Spain |
| Buchtitel: | Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2019) |
| Reihe: | CEUR Workshop Proceedings |
| Veranstaltungsort: | Bilbao, Spain |
| URL / URN: | http://ceur-ws.org/Vol-2421/HAHA_paper_6.pdf |
| Zugehörige Links: | |
| Kurzbeschreibung (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. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Graduiertenkolleg 1994 Adaptive Informationsaufbereitung aus heterogenen Quellen |
| Hinterlegungsdatum: | 09 Jul 2019 09:57 |
| Letzte Änderung: | 24 Jan 2020 12:03 |
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