Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning
This work addresses humour processing for natural language processing applications, but it is incremental as it adapts an existing method to a new language dataset.
The paper tackles the problem of predicting humorousness in tweets by applying a Gaussian process preference learning variant to rank and rate short texts using human preferences and linguistic annotations, reporting performance on Spanish-language data from the HAHA@IberLEF2019 campaign for humour detection and funniness score prediction.
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.