Abstract
Humor recognition is a challenging task in natural language processing. This document presents my approaches to detect and rate humor and offense from the given text. This task includes 2 tasks: task 1 which contains 3 subtasks (1a, 1b, and 1c), and task 2. Subtask 1a and 1c can be regarded as classification problems and take ALBERT as the basic model. Subtask 1b and 2 can be viewed as regression issues and take RoBERTa as the basic model.- Anthology ID:
- 2021.semeval-1.156
- 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:
- 1120–1124
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.156
- DOI:
- 10.18653/v1/2021.semeval-1.156
- Cite (ACL):
- Maoqin Yang. 2021. Gulu at SemEval-2021 Task 7: Detecting and Rating Humor and Offense. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 1120–1124, Online. Association for Computational Linguistics.
- Cite (Informal):
- Gulu at SemEval-2021 Task 7: Detecting and Rating Humor and Offense (Yang, SemEval 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.semeval-1.156.pdf