AMBER is a model of first language acquisition that improves its performance through a process of error recovery. The model is implemented as an adaptive production system that introduces new condition-action rules on the basis of experience. AMBER starts with the ability to say only one word at a time, but adds rules for ordering goals and producing grammatical morphemes, based on comparisons between predicted and observed sentences. The morpheme rules may be overly general and lead to errors of commission; such errors evoke a discrimination process, producing more conservative rules with additional conditions. The system's performance improves gradually, since rules must be relearned many times before they are used. AMBER'S learning mechanisms account for some of the major developments observed in children's early speech.
CITATION STYLE
Langley, P. (1982). A model of early syntactic development. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (Vol. 1982-June, pp. 145–151). Association for Computational Linguistics (ACL). https://doi.org/10.3115/981251.981290
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