Which iR Model has a BetteR sense of huMoR? seaRch oveR a laRge collection of Jokes

This paper describes experiments on humorous response generation for short text conversations. Firstly, we compiled a collection of 63,000 jokes from online social networks (VK and Twitter). Secondly, we implemented several context-aware joke retrieval models: BM25 as a baseline, query term reweighting, word2vec-based model, and learning-to-rank approach with multiple features. Finally, we evaluated these models in two ways: on the community question answering platform [email protected] and in laboratory settings. Evaluation shows that an information retrieval approach to humorous response generation yields satisfactory performance.