@inproceedings{desetty-etal-2020-hasyarasa,
title = "Hasyarasa at {S}em{E}val-2020 Task 7: Quantifying Humor as Departure from Expectedness",
author = "Desetty, Ravi Theja and
Chatterjee, Ranit and
Ghaisas, Smita",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.105",
doi = "10.18653/v1/2020.semeval-1.105",
pages = "833--842",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Hasyarasa at SemEval-2020 Task 7: Quantifying Humor as Departure from Expectedness
%A Desetty, Ravi Theja
%A Chatterjee, Ranit
%A Ghaisas, Smita
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F desetty-etal-2020-hasyarasa
%X 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.
%R 10.18653/v1/2020.semeval-1.105
%U https://aclanthology.org/2020.semeval-1.105
%U https://doi.org/10.18653/v1/2020.semeval-1.105
%P 833-842
Markdown (Informal)
[Hasyarasa at SemEval-2020 Task 7: Quantifying Humor as Departure from Expectedness](https://aclanthology.org/2020.semeval-1.105) (Desetty et al., SemEval 2020)
ACL