Abstract
Everyday natural language communication is normally successful, even though contemporary computational linguistics has shown that NL is characterised by very high degree of ambiguity and the results of stochastic methods are not good enough to explain the high success rate. Bayesian natural language interpretation and the combination with speaker self-monitoring are proposed as an explanation of the high success rates. The consequences of the model for language learning are briefly explored (inhibitory effects of production in understanding can only emerge when production is good enough, and inhibitory effects of comprehension in production only when comprehension is good enough) and applied to production-comprehension asymmetries.
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Zeevat, H. (2011). Bayesian NL Interpretation and Learning. In: Bezhanishvili, N., Löbner, S., Schwabe, K., Spada, L. (eds) Logic, Language, and Computation. TbiLLC 2009. Lecture Notes in Computer Science(), vol 6618. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22303-7_22
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DOI: https://doi.org/10.1007/978-3-642-22303-7_22
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