Modelling Child Learning and Parsing of Long-range Syntactic Dependencies

• URL: http://arxiv.org/abs/2503.12832v1
• Date: Mon, 17 Mar 2025 05:24:39 GMT
• Title: Modelling Child Learning and Parsing of Long-range Syntactic Dependencies
• Authors: Louis Mahon, Mark Johnson, Mark Steedman,
• Abstract summary: This work develops a probabilistic child language acquisition model to learn a range of linguistic phenonmena.<n>The model is trained on a corpus of real child-directed speech, where each utterance is paired with a logical form as a meaning representation.<n>After training, the model can deduce the correct parse tree and word meanings for a given utterance-meaning pair.
• Score: 13.365038484517076
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This work develops a probabilistic child language acquisition model to learn a range of linguistic phenonmena, most notably long-range syntactic dependencies of the sort found in object wh-questions, among other constructions. The model is trained on a corpus of real child-directed speech, where each utterance is paired with a logical form as a meaning representation. It then learns both word meanings and language-specific syntax simultaneously. After training, the model can deduce the correct parse tree and word meanings for a given utterance-meaning pair, and can infer the meaning if given only the utterance. The successful modelling of long-range dependencies is theoretically important because it exploits aspects of the model that are, in general, trans-context-free.

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