  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.
