Abstract
This paper describes an ensemble model designed for Semeval-2020 Task 7. The task is based on the Humicroedit dataset that is comprised of news titles and one-word substitutions designed to make them humorous. We use BERT, FastText, Elmo, and Word2Vec to encode these titles then pass them to a bidirectional gated recurrent unit (BiGRU) with attention. Finally, we used XGBoost on the concatenation of the results of the different models to make predictions.- Anthology ID:
- 2020.semeval-1.110
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Venue:
- SemEval
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 871–875
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.110
- DOI:
- 10.18653/v1/2020.semeval-1.110
- Cite (ACL):
- Joseph Tomasulo, Jin Wang, and Xuejie Zhang. 2020. YNU-HPCC at SemEval-2020 Task 7: Using an Ensemble BiGRU Model to Evaluate the Humor of Edited News Titles. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 871–875, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- YNU-HPCC at SemEval-2020 Task 7: Using an Ensemble BiGRU Model to Evaluate the Humor of Edited News Titles (Tomasulo et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2020.semeval-1.110.pdf
- Data
- Humicroedit