Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks

Annual Meeting of the Association for Computational Linguistics (ACL)


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.

Related Publications

All Publications

LEEP: A New Measure to Evaluate Transferability of Learned Representations

Cuong V. Nguyen, Tal Hassner, Matthias Seeger, Cedric Archambeau

ICML - July 13, 2020

The Differentiable Cross-Entropy Method

Brandon Amos, Denis Yarats

ICML - July 12, 2020

Growing Action Spaces

Gregory Farquhar, Laura Gustafson, Zeming Lin, Shimon Whiteson, Nicolas Usunier, Gabriel Synnaeve

July 14, 2020

Stochastic Hamiltonian Gradient Methods for Smooth Games

Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas

ICML - July 12, 2020

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy