Abstract
This paper describes my efforts in evaluating how editing news headlines can make them funnier within the frames of SemEval 2020 Task 7. I participated in both of the sub-tasks: Sub-Task 1 “Regression” and Sub-task 2 “Predict the funnier of the two edited versions of an original headline”. I experimented with a number of different models, but ended up using DeepPavlov logistic regression (LR) with BERT English cased embeddings for the first sub-task and support vector regression model (SVR) for the second. RMSE score obtained for the first task was 0.65099 and accuracy for the second – 0.32915.- Anthology ID:
- 2020.semeval-1.138
- 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:
- 1055–1059
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.138
- DOI:
- 10.18653/v1/2020.semeval-1.138
- Cite (ACL):
- Anita Soloveva. 2020. SO at SemEval-2020 Task 7: DeepPavlov Logistic Regression with BERT Embeddings vs SVR at Funniness Evaluation. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1055–1059, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- SO at SemEval-2020 Task 7: DeepPavlov Logistic Regression with BERT Embeddings vs SVR at Funniness Evaluation (Soloveva, SemEval 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.semeval-1.138.pdf