Abstract
Model ensemble techniques often increase task performance in neural networks; however, they require increased time, memory, and management effort. In this study, we propose a novel method that replicates the effects of a model ensemble with a single model. Our approach creates K-virtual models within a single parameter space using K-distinct pseudo-tags and K-distinct vectors. Experiments on text classification and sequence labeling tasks on several datasets demonstrate that our method emulates or outperforms a traditional model ensemble with 1/K-times fewer parameters.- Anthology ID:
- 2020.acl-main.271
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3006–3013
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.271
- DOI:
- 10.18653/v1/2020.acl-main.271
- Cite (ACL):
- Ryosuke Kuwabara, Jun Suzuki, and Hideki Nakayama. 2020. Single Model Ensemble using Pseudo-Tags and Distinct Vectors. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3006–3013, Online. Association for Computational Linguistics.
- Cite (Informal):
- Single Model Ensemble using Pseudo-Tags and Distinct Vectors (Kuwabara et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2020.acl-main.271.pdf
- Data
- IMDb Movie Reviews, RCV1