Deep learnability: using neural networks to quantify language similarity and learnability

Cohen, C. , Higham, C. F. and Nabi, S. W. (2020) Deep learnability: using neural networks to quantify language similarity and learnability. Frontiers in Artificial Intelligence, 3, 43. (doi: 10.3389/frai.2020.00043)

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Learning a second language (L2) usually progresses faster if a learner's L2 is similar to their first language (L1). Yet global similarity between languages is difficult to quantify, obscuring its precise effect on learnability. Further, the combinatorial explosion of possible L1 and L2 language pairs, combined with the difficulty of controlling for idiosyncratic differences across language pairs and language learners, limits the generalisability of the experimental approach. In this study, we present a different approach, employing artificial languages and artificial learners. We built a set of five artificial languages whose underlying grammars and vocabulary were manipulated to ensure a known degree of similarity between each pair of languages. We next built a series of neural network models for each language, and sequentially trained them on pairs of languages. These models thus represented L1 speakers learning L2s. By observing the change in activity of the cells between the L1-speaker model and the L2-learner model, we estimated how much change was needed for the model to learn the new language. We then compared the change for each L1/L2 bilingual model to the underlying similarity across each language pair. The results showed that this approach can not only recover the facilitative effect of similarity on L2 acquisition, but can also offer new insights into the differential effects across different domains of similarity. These findings serve as a proof of concept for a generalisable approach that can be applied to natural languages.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Higham, Dr Catherine and Cohen, Dr Clara and Nabi, Dr Syed Waqar
Authors: Cohen, C., Higham, C. F., and Nabi, S. W.
College/School:College of Arts & Humanities > School of Critical Studies > English Language and Linguistics
College of Science and Engineering > School of Computing Science
Journal Name:Frontiers in Artificial Intelligence
Publisher:Frontiers Media
ISSN (Online):2624-8212
Copyright Holders:Copyright © 2020 Cohen, Higham and Nabi
First Published:First published in Frontiers in Artificial Intelligence 3: 43
Publisher Policy:Reproduced under a Creative Commons License
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
190841UK Quantum Technology Hub in Enhanced Quantum ImagingMiles PadgettEngineering and Physical Sciences Research Council (EPSRC)EP/M01326X/1P&S - Physics & Astronomy