Baby SRL: Modeling early language acquisition

Michael Connor, Yael Gertner, Cynthia Fisher, Dan Roth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A fundamental task in sentence comprehension is to assign semantic roles to sentence constituents. The structure-mapping account proposes that children start with a shallow structural analysis of sentences: children treat the number of nouns in the sentence as a cue to its semantic predicateargument structure, and represent language experience in an abstract format that permits rapid generalization to new verbs. In this paper, we tested the consequences of these representational assumptions via experiments with a system for automatic semantic role labeling (SRL), trained on a sample of child-directed speech. When the SRL was presented with representations of sentence structure consisting simply of an ordered set of nouns, it mimicked experimental findings with toddlers, including a striking error found in children. Adding features representing the position of the verb increased accuracy and eliminated the error. We show the SRL system can use incremental knowledge gain to switch from error-prone noun order features to a more accurate representation, demonstrating a possible mechanism for this process in child development.

Original languageEnglish (US)
Title of host publicationCoNLL 2008 - Proceedings of the Twelfth Conference on Computational Natural Language Learning
Pages81-88
Number of pages8
StatePublished - 2008
Event12th Conference on Computational Natural Language Learning, CoNLL 2008 - Manchester, United Kingdom
Duration: Aug 16 2008Aug 17 2008

Publication series

NameCoNLL 2008 - Proceedings of the Twelfth Conference on Computational Natural Language Learning

Other

Other12th Conference on Computational Natural Language Learning, CoNLL 2008
Country/TerritoryUnited Kingdom
CityManchester
Period8/16/088/17/08

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Linguistics and Language

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