Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks

Yi Tay, Aston Zhang, Anh Tuan Luu, Jinfeng Rao, Shuai Zhang, Shuohang Wang, Jie Fu, Siu Cheung Hui


Abstract
Many state-of-the-art neural models for NLP are heavily parameterized and thus memory inefficient. This paper proposes a series of lightweight and memory efficient neural architectures for a potpourri of natural language processing (NLP) tasks. To this end, our models exploit computation using Quaternion algebra and hypercomplex spaces, enabling not only expressive inter-component interactions but also significantly (75%) reduced parameter size due to lesser degrees of freedom in the Hamilton product. We propose Quaternion variants of models, giving rise to new architectures such as the Quaternion attention Model and Quaternion Transformer. Extensive experiments on a battery of NLP tasks demonstrates the utility of proposed Quaternion-inspired models, enabling up to 75% reduction in parameter size without significant loss in performance.
Anthology ID:
P19-1145
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1494–1503
Language:
URL:
https://aclanthology.org/P19-1145
DOI:
10.18653/v1/P19-1145
Bibkey:
Cite (ACL):
Yi Tay, Aston Zhang, Anh Tuan Luu, Jinfeng Rao, Shuai Zhang, Shuohang Wang, Jie Fu, and Siu Cheung Hui. 2019. Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1494–1503, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks (Tay et al., ACL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/fix-dup-bibkey/P19-1145.pdf
Code
 vanzytay/QuaternionTransformers
Data
IMDb Movie ReviewsMultiNLISNLISSTUDCWikiQA