Abstract
During the course of first language acquisition, children produce linguistic forms that do not conform to adult grammar. In this paper, we introduce a data set and approach for systematically modeling this child-adult grammar divergence. Our corpus consists of child sentences with corrected adult forms. We bridge the gap between these forms with a discriminatively reranked noisy channel model that translates child sentences into equivalent adult utterances. Our method outperforms MT and ESL baselines, reducing child error by 20%. Our model allows us to chart specific aspects of grammar development in longitudinal studies of children, and investigate the hypothesis that children share a common developmental path in language acquisition.- Anthology ID:
- Q13-1011
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 1
- Month:
- Year:
- 2013
- Address:
- Cambridge, MA
- Editors:
- Dekang Lin, Michael Collins
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 125–138
- Language:
- URL:
- https://aclanthology.org/Q13-1011
- DOI:
- 10.1162/tacl_a_00215
- Cite (ACL):
- Sam Sahakian and Benjamin Snyder. 2013. Modeling Child Divergences from Adult Grammar. Transactions of the Association for Computational Linguistics, 1:125–138.
- Cite (Informal):
- Modeling Child Divergences from Adult Grammar (Sahakian & Snyder, TACL 2013)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/Q13-1011.pdf