Duluth at SemEval-2020 Task 7: Using Surprise as a Key to Unlock Humorous Headlines
Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:QTZKOYPQrecord.jsonopen to challenge →
classification
cs.CL
keywords
headlineseditedgetssemeval-2020submissionssubtasksurprisesystem
read the original abstract
We use pretrained transformer-based language models in SemEval-2020 Task 7: Assessing the Funniness of Edited News Headlines. Inspired by the incongruity theory of humor, we use a contrastive approach to capture the surprise in the edited headlines. In the official evaluation, our system gets 0.531 RMSE in Subtask 1, 11th among 49 submissions. In Subtask 2, our system gets 0.632 accuracy, 9th among 32 submissions.
This paper has not been read by Pith yet.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.