Language acquisition and processing has been one of the central research issues in cognitive science. It is also an area in which the use of cognitive computational modelling has been especially intense. Language, and especially language acquisition, has been the key battleground for nativists and empiricists; and between advocates of rule-based, probabilistic, and connectionist models of thought. Yet the computational models proposed by CogSci researchers are often far behind, in scale and accuracy, the non-cognitively motivated models proposed by computational linguists, which are heavily based on machine learning techniques.
This workshop asks how far these techniques, and their theoretical underpinnings, provide tools for building richer theories of cognitive processes. For example, can powerful machine learning techniques (e.g. kernel methods) help build models of the cognitive operations involved in human language acquisition? Conversely, can insights from cognitive science help inform and focus computational linguistic and machine learning? Can evidence concerning the spectacular computational performance of the human language processor help inspire new generations of computational linguistic and machine learning tools?
This workshop will bring together participants from all of the disciplines that address this problem to discuss a range of related topics from methodological issues in computational modelling of language acquisition, including evaluation of empirical learning models, to technical problems in machine learning and grammatical inference. The workshop includes invited talks by some of the leading researchers in these fields.