Abstract
This paper describes our contribution to SemEval 2019 Task 5: Hateval. We propose to investigate how domain-specific text classification task can benefit from pretrained state of the art language models and how they can be combined with classical handcrafted features. For this purpose, we propose an approach based on a feature-level Meta-Embedding to let the model choose which features to keep and how to use them.- Anthology ID:
- S19-2083
- Volume:
- Proceedings of the 13th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota, USA
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 469–475
- Language:
- URL:
- https://aclanthology.org/S19-2083
- DOI:
- 10.18653/v1/S19-2083
- Cite (ACL):
- Miriam Benballa, Sebastien Collet, and Romain Picot-Clemente. 2019. Saagie at Semeval-2019 Task 5: From Universal Text Embeddings and Classical Features to Domain-specific Text Classification. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 469–475, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- Saagie at Semeval-2019 Task 5: From Universal Text Embeddings and Classical Features to Domain-specific Text Classification (Benballa et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/S19-2083.pdf
- Data
- GLUE