Funny3 at SemEval-2020 Task 7: Humor Detection of Edited Headlines with LSTM and TFIDF Neural Network System

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 1013–1018 Barcelona, Spain (Online), December 12, 2020. 1013 funny3 at SemEval-2020 Task 7: Humor Detection of Edited Headlines with LSTM and TFIDF Neural Network System Xuefeng Luo, Kuan Tang Linguistics Department, University of Tuebingen, Germany firstname.lastname@student.uni-tuebingen.de Abstract This paper presents a neural network system where we participate in the first task of SemEval-2020 shared task 7 “Assessing the Funniness of Edited News Headlines”. Our target is to create a neural network model that predicts the funniness of edited headlines. We build our model using a combination of LSTM and TF-IDF, then a feed-forward neural network. The system manages to slightly improve RSME scores regarding our mean score baseline.1 1 Introduction How the computer recognizes, generates, and analyzes humor is an interesting but meaningful challenge for artificial intelligence (AI). Not only creativity and sophistication of language are needed for the sense of humor, but also world knowledge, empathy, and cognitive mechanisms which are not so easy for technologists to model theoretically. Several stable and capacity large enough datasets have been built for training models in computational humor studies. These datasets may contain different contexts. Mihalcea Strapparava (2006) set up funny one-liners which contain more than 24,000 onliner jokes. Filatova (2012) collects regular and sarcastic Amazon product reviews to identify sarcasm on a sentence level or for a specific phrase. Khodak et al. (2017) introduce the Self-Annotated Reddit Corpus (SARC) which is a corpus that has 1.3 million sarcastic statements for training and evaluating systems for sarcasm detection. Hossain et al. (2017) set up Mad Libs, which is a popular fill-in-the-blank game judged by the human. And then, Hossain et al. (2019) introduce a novel dataset Humicroedit that contains original media news headlines from an online posted website Reddit and enables several humor tasks for humor understanding, predicting, generating, recommending and ranking. The latest one is FunLines, which is a competitive game that not only allows players to edit news headlines but also rate the funniness of headlines edited by others. Each dataset has its unique purpose, but how to understand headlines and what makes a few words in one headline so funny are becoming new problems for AI. In this paper, we want to test how machines understand humor generated by such short edits rely on Humicroedit to predict the mean funniness of edited headlines. 2 Related work How to generate humor is not as easy as we think, especially for computers. Binsted et al. (1997) developed a formal model producing riddles and implemented it in jape, a computer program which generates simple punning riddles and confirmed that jape’s output texts are indeed jokes after evaluating the program by 120 children. Stock and Strapparava (2002) established HAHAcronym to realize the irony reanalysis and generation of acronyms in a concentrated but unrestricted context. Petrovic and Matthews (2013) rely on large quantities of unlabeled data to deal with generating a fixed syntactic structure – [I like my X] like [I like my Y], …

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