Abstract
An understanding of humor is an essential component of human-facing NLP systems. In this paper, we investigate several methods for detecting humor in short statements as part of Semeval-2021 Shared Task 7. For Task 1a, we apply an ensemble of fine-tuned pre-trained language models; for Tasks 1b, 1c, and 2a, we investigate various tree-based and linear machine learning models. Our final system achieves an F1-score of 0.9571 (ranked 24 / 58) on Task 1a, an RMSE of 0.5580 (ranked 18 / 50) on Task 1b, an F1-score of 0.5024 (ranked 26 / 36) on Task 1c, and an RMSE of 0.7229 (ranked 45 / 48) on Task 2a.- Anthology ID:
- 2021.semeval-1.171
- Volume:
- Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venue:
- SemEval
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1209–1214
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.171
- DOI:
- 10.18653/v1/2021.semeval-1.171
- Cite (ACL):
- Nathan Chi and Ryan Chi. 2021. RedwoodNLP at SemEval-2021 Task 7: Ensembled Pretrained and Lightweight Models for Humor Detection. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 1209–1214, Online. Association for Computational Linguistics.
- Cite (Informal):
- RedwoodNLP at SemEval-2021 Task 7: Ensembled Pretrained and Lightweight Models for Humor Detection (Chi & Chi, SemEval 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.semeval-1.171.pdf