To be fair: A case for cognitively-inspired models of meaning

In the last years, the cost of Natural Language Processing algorithms has become more and more evident. That cost has many facets, including training times, storage, replicability, interpretability, equality of access to experimental paradigms, and even environmental impact. In this paper, we review the requirements of a ‘good’ model and argue that a move is needed towards lightweight and interpretable implementations, which promote scientific fairness, paradigmatic diversity, and ultimately foster applications available to all, regardless of financial prosperity. We propose that the community still has much to learn from cognitively-inspired algorithms, which often show extreme efficiency and can ‘run’ on very simple organisms. As a case study, we investigate the fruit fly’s olfactory system as a distributional semantics model. We show that, even in its rawest form, it provides many of the features that we might require from an ideal model of meaning acquisition. 1