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.

本文针对自然语言处理（NLP）任务，提出了一系列轻量级和内存高效的神经网络模型，利用四元数代数和超复数空间进行计算，在不影响性能的情况下减少了模型参数的大小达到 75%。作者提出了四元数变体模型和新的架构，如四元数注意力模型和四元数变换器模型，并在多项 NLP 任务上进行了广泛的实验，证明了所提出的模型的优越性。