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Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning

This project will contain source code and data for a Gaussian Process Preference Learning (GPPL) system for predicting the humorousness of tweets, as described in the following paper:

Tristan Miller, Erik-Lân Do Dinh, Edwin Simpson, and Iryna Gurevych. Predicting the humorousness of tweets using Gaussian process preference learning. Procesamiento del Lenguaje Natural, 64:37–44, March 2020. ISSN 1135-5948. DOI: 10.26342/2020-64-4.

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 is similar to one that 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.

  author  = {Tristan Miller and Do Dinh, Erik-L{\^{a}}n and Edwin Simpson and Iryna Gurevych},
  title   = {Predicting the Humorousness of Tweets Using {Gaussian} Process Preference Learning},
  journal = {Procesamiento del Lenguaje Natural},
  volume  = {64},
  pages   = {37--44},
  month   = mar,
  year    = {2020},
  issn    = {1135-5948},
  doi     = {10.26342/2020-64-4},

Due to circumstances beyond our control, the preparation and deployment of the source code and data for this project have been delayed. Please bear with us a while longer.


Contact person: Tristan Miller


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