The paper describes a work in progress on humorous response generation for short-text conversation using information retrieval approach. We gathered a large collection of funny tweets and implemented three baseline retrieval models: BM25, the query term reweighting model based on syntactic parsing and named entity recognition, and the doc2vec similarity model. We evaluated these models in two ways: in situ on a popular community question answering platform and in laboratory settings. The approach proved to be promising: even simple search techniques demonstrated satisfactory performance. The collection, test questions, evaluation protocol, and assessors’ judgments create a ground for future research towards more sophisticated models.
CITATION STYLE
Blinov, V., Mishchenko, K., Bolotova, V., & Braslavski, P. (2017). A pinch of humor for short-text conversation: An information retrieval approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10456 LNCS, pp. 3–15). Springer Verlag. https://doi.org/10.1007/978-3-319-65813-1_1
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