Universal linguistic inductive biases via meta-learning

• URL: http://arxiv.org/abs/2006.16324v1
• Date: Mon, 29 Jun 2020 19:15:10 GMT
• Title: Universal linguistic inductive biases via meta-learning
• Authors: R. Thomas McCoy, Erin Grant, Paul Smolensky, Thomas L. Griffiths, Tal Linzen
• Abstract summary: It is unclear which inductive biases can explain observed patterns in language acquisition. We introduce a framework for giving linguistic inductive biases to a neural network model. We demonstrate this framework with a case study based on syllable structure.
• Score: 36.43388942327124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: How do learners acquire languages from the limited data available to them? This process must involve some inductive biases - factors that affect how a learner generalizes - but it is unclear which inductive biases can explain observed patterns in language acquisition. To facilitate computational modeling aimed at addressing this question, we introduce a framework for giving particular linguistic inductive biases to a neural network model; such a model can then be used to empirically explore the effects of those inductive biases. This framework disentangles universal inductive biases, which are encoded in the initial values of a neural network's parameters, from non-universal factors, which the neural network must learn from data in a given language. The initial state that encodes the inductive biases is found with meta-learning, a technique through which a model discovers how to acquire new languages more easily via exposure to many possible languages. By controlling the properties of the languages that are used during meta-learning, we can control the inductive biases that meta-learning imparts. We demonstrate this framework with a case study based on syllable structure. First, we specify the inductive biases that we intend to give our model, and then we translate those inductive biases into a space of languages from which a model can meta-learn. Finally, using existing analysis techniques, we verify that our approach has imparted the linguistic inductive biases that it was intended to impart.

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