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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.semeval-1.139.pdf