Evaluating systematicity in neural networks with natural language inference
- Emily Goodwin, Dept Linguistics, McGill University, Montreal, Quebec, Canada
- Koustuv Sinha, Department of Linguistics, McGill University, Montreal, Quebec, Canada
- Timothy O’Donnell, Department of Linguistics, McGill University, Montreal, Quebec, Canada
AbstractCompositionality makes linguistic creativity possible. By combining words, we can express uncountably many thoughts; by learning new words, we can extend the system and express a vast number of new thoughts. Recently, a number of studies have questioned the ability of neural networks to generalize compositionally (Dasgupta, Guo, Gershman & Goodman, 2018). We extend this line of work by systematically investigating the way in which these systems generalize novel words. In the setting of a simple system for natural language inference, natural logic (McCartney & Manning, 2007), we systematically explore the generalization capabilities of various neural network architectures. We identify several key properties of a compositional system, and develop metrics to test them. We show that these architectures do not generalize in human-like ways, lacking inductive leaps characteristic of human learning.
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