Abstract
This paper presents a neural network system where we participate in the first task of SemEval-2020 shared task 7 “Assessing the Funniness of Edited News Headlines”. Our target is to create to neural network model that can predict the funniness of edited headlines. We build our model using a combination of LSTM and TF-IDF, then a feed-forward neural network. The system manages to slightly improve RSME scores regarding our mean score baseline.- Anthology ID:
- 2020.semeval-1.132
- 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:
- 1013–1018
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.132
- DOI:
- 10.18653/v1/2020.semeval-1.132
- Cite (ACL):
- Xuefeng Luo and Kuan Tang. 2020. Funny3 at SemEval-2020 Task 7: Humor Detection of Edited Headlines with LSTM and TFIDF Neural Network System. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1013–1018, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- Funny3 at SemEval-2020 Task 7: Humor Detection of Edited Headlines with LSTM and TFIDF Neural Network System (Luo & Tang, SemEval 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2020.semeval-1.132.pdf
- Data
- Humicroedit, SARC