KDEhumor at SemEval-2020 Task 7: A Neural Network Model for Detecting Funniness in Dataset Humicroedit
Rida Miraj, Masaki Aono · 2020
This paper describes our contribution to SemEval-2020 Task 7: Assessing Humor in Edited News Headlines.Here we present a method based on a deep neural network.In recent years, quite some attention has been devoted to humor production and perception.Our team KdeHumor employs recurrent neural network models including Bi-Directional LSTMs (BiLSTMs).Moreover, we utilize the state-of-the-art pre-trained sentence embedding techniques.We analyze the performance of our method and demonstrate the contribution of each component of our architecture.