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 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.
Cite
CITATION STYLE
Rozental, A., Biton, D., & Blank, I. (2020). Amobee at SemEval-2020 Task 7: Regularization of Language Model Based Classifiers. In 14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings (pp. 981–985). International Committee for Computational Linguistics. https://doi.org/10.18653/v1/2020.semeval-1.127
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
