Abstract
This paper describes Amobee’s participation in SemEval-2020 task 7: “Assessing Humor in Edited News Headlines”, sub-tasks 1 and 2. The goal of this task was to estimate the funniness of human modified news headlines. in this paper we present methods to fine-tune and ensemble various language models (LM) based classifiers to for this task. This technique used for both sub-tasks and reached the second place (out of 49) in sub-tasks 1 with RMSE score of 0.5, and the second (out of 32) place in sub-task 2 with accuracy of 66% without using any additional data except the official training set.- Anthology ID:
- 2020.semeval-1.127
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 981–985
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.127
- DOI:
- 10.18653/v1/2020.semeval-1.127
- Cite (ACL):
- Alon Rozental, Dadi Biton, and Ido Blank. 2020. Amobee at SemEval-2020 Task 7: Regularization of Language Model Based Classifiers. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 981–985, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- Amobee at SemEval-2020 Task 7: Regularization of Language Model Based Classifiers (Rozental et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2020.semeval-1.127.pdf