kdehumor at semeval-2020 task 7: a neural network model for detecting funniness in dataset humicroedit

• URL: http://arxiv.org/abs/2105.05135v1
• Date: Tue, 11 May 2021 15:44:03 GMT
• Title: kdehumor at semeval-2020 task 7: a neural network model for detecting funniness in dataset humicroedit
• Authors: Rida Miraj, Masaki Aono
• Abstract summary: Team KdeHumor employs recurrent neural network models including Bi-Directional LSTMs (BiLSTMs) We analyze the performance of our method and demonstrate the contribution of each component of our architecture.
• Score: 3.612189440297043
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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