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
To be fair: A case for cognitively-inspired models of meaning / Preissner, S.; Herbelot, A.. - 2481:(2019). (Intervento presentato al convegno 6th Italian Conference on Computational Linguistics, CLiC-it 2019 tenutosi a ita nel 2019).
To be fair: A case for cognitively-inspired models of meaning
Herbelot A.
2019-01-01
Abstract
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. 1I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione