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Differencial use of implicit negative evidence in generative and discriminative language learning (2009)
| Venue: | In Advances in Neural Information Processing Systems 22 |
| Citations: | 14 - 4 self |
BibTeX
@INPROCEEDINGS{Hsu09differencialuse,
author = {Anne S. Hsu and Thomas L. Griffiths},
title = {Differencial use of implicit negative evidence in generative and discriminative language learning},
booktitle = {In Advances in Neural Information Processing Systems 22},
year = {2009}
}
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Abstract
A classic debate in cognitive science revolves around understanding how children learn complex linguistic rules, such as those governing restrictions on verb alternations, without negative evidence. Traditionally, formal learnability arguments have been used to claim that such learning is impossible without the aid of innate language-specific knowledge. However, recently, researchers have shown that statistical models are capable of learning complex rules from only positive evidence. These two kinds of learnability analyses differ in their assumptions about the distribution from which linguistic input is generated. The former analyses assume that learners seek to identify grammatical sentences in a way that is robust to the distribution from which the sentences are generated, analogous to discriminative approaches in machine learning. The latter assume that learners are trying to estimate a generative model, with sentences being sampled from that model. We show that these two learning approaches differ in their use of implicit negative evidence – the absence of a sentence – when learning verb alternations, and demonstrate that human learners can produce results consistent with the predictions of both approaches, depending on how the learning problem is presented. 1
Keyphrases
implicit negative evidence discriminative language learning differencial use verb alternation discriminative approach learning approach generative model human learner formal learnability argument positive evidence learnability analysis complex rule former analysis latter assume complex linguistic rule classic debate learning problem grammatical sentence statistical model linguistic input negative evidence innate language-specific knowledge machine learning cognitive science revolves
