Hasyarasa at SemEval-2020 Task 7: Quantifying Humor as Departure from Expectedness

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 833–842 Barcelona, Spain (Online), December 12, 2020. 833 Hasyarasa at SemEval-2020 Task 7: Quantifying Humor as departure from Expectedness Ravi Theja Desetty TCS Research, India rt.desetty@tcs.com Ranit Chatterjee TCS Research, India ranit.chatterjee@tcs.com Smita Ghaisas TCS Research, India smita.ghaisas@tcs.com Abstract This paper describes our system submission Hasyarasa for the SemEval-2020 Task-7: Assessing Humor in Edited News Headlines. This task has two subtasks. The goal of Subtask 1 is to predict the mean funniness of the edited headline given the original and the edited headline. In Subtask 2, given two edits on the original headline, the goal is to predict the funnier of the two. We observed that the departure from expected state/ actions of situations/ individuals is the cause of humor in the edited headlines. We propose two novel features: Contextual Semantic Distance and Contextual Neighborhood Distance to estimate this departure and thus capture the contextual absurdity and hence the humor in the edited headlines. We have used these features together with a Bi-LSTM Attention based model and have achieved 0.53310 RMSE for Subtask 1 and 60.19% accuracy for Subtask 2. 1 Introduction Humor is a highly intellectual and communicative activity that promotes laughter and provides amusement. A great sense of humor refers to the ability to perceive things as funny. With the advances in Natural Language Processing (NLP) techniques, there has been considerable research effort in developing artificial sense of humor in machines. Much of the existing work on humor recognition is done in evaluating whether a given text is humorous or non-humorous using datasets such as twitter, yelp reviews, reddit jokes and other sources (Barbieri and Saggion, 2014; Weller and Seppi, 2019). However, the problem dealt in SemEval-2020 Task 7: Assessing the Funniness of Edited News Headlines is not about classifying a given text as humorous or non-humorous. It is about how a micro-edit on a news headline can change it from non-humorous to humorous (Hossain et al., 2020a; Hossain et al., 2019). There are two subtasks in this task. Subtask 1: Given the original and edited headline, the task is to predict the mean funniness of the edited headline. Subtask 2: Given the original headline and the two edited versions of the original headline, the task is to predict which edited version is the funnier of the two edited versions. The evaluation criteria, as set by the organizers for Subtask 1 is Root Mean Squared Error (RMSE) and for Subtask 2 is accuracy. In this work, our contribution to the challenge is to model two novel features namely Contextual Semantic Distance (CSD), and Contextual Neighborhood Distance (CND) which are based on our observation that the departure from expected state/ actions of the situations/ individuals is the cause of humor in the edited news headlines. The remainder of the paper is organized as follows: In Section 2, we present related work. In Section 3, we give a brief description of Hasyrasa - our system for quantifying humor, discuss the …

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