YNU-HPCC at SemEval-2020 Task 7: Using an Ensemble BiGRU Model to Evaluate the Humor of Edited News Titles

Joseph Tomasulo, Jin Wang, Xuejie Zhang


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)
Editors:
Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
Venue:
SemEval
SIG:
SIGLEX
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.110.pdf
Data
Humicroedit