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

Ravi Theja Desetty, Ranit Chatterjee, Smita Ghaisas


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.
Anthology ID:
2020.semeval-1.105
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Editors:
Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
Venue:
SemEval
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
833–842
Language:
URL:
https://aclanthology.org/2020.semeval-1.105
DOI:
10.18653/v1/2020.semeval-1.105
Bibkey:
Cite (ACL):
Ravi Theja Desetty, Ranit Chatterjee, and Smita Ghaisas. 2020. Hasyarasa at SemEval-2020 Task 7: Quantifying Humor as Departure from Expectedness. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 833–842, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
Hasyarasa at SemEval-2020 Task 7: Quantifying Humor as Departure from Expectedness (Desetty et al., SemEval 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.105.pdf