UniTuebingenCL at SemEval-2020 Task 7: Humor Detection in News Headlines

Charlotte Ammer, Lea Grüner


Abstract
This paper describes the work done by the team UniTuebingenCL for the SemEval 2020 Task 7: “Assessing the Funniness of Edited News Headlines”. We participated in both sub-tasks: sub-task A, given the original and the edited headline, predicting the mean funniness of the edited headline; and sub-task B, given the original headline and two edited versions, predicting which edited version is the funnier of the two. A Ridge Regression model using Elmo and Glove embeddings as well as Truncated Singular Value Decomposition was used as the final model. A long short term memory model recurrent network (LSTM) served as another approach for assessing the funniness of a headline. For the first sub-task, we experimented with the extraction of multiple features to achieve lower Root Mean Squared Error. The lowest Root Mean Squared Error achieved was 0.575 for sub-task A, and the highest Accuracy was 0.618 for sub-task B.
Anthology ID:
2020.semeval-1.139
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
1060–1065
Language:
URL:
https://aclanthology.org/2020.semeval-1.139
DOI:
10.18653/v1/2020.semeval-1.139
Bibkey:
Cite (ACL):
Charlotte Ammer and Lea Grüner. 2020. UniTuebingenCL at SemEval-2020 Task 7: Humor Detection in News Headlines. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1060–1065, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
UniTuebingenCL at SemEval-2020 Task 7: Humor Detection in News Headlines (Ammer & Grüner, SemEval 2020)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2020.semeval-1.139.pdf