Cognitively Plausible Models of Human Language Processing

We pose the development of cognitively plausible models of human language pro-cessing as a challenge for computational linguistics. Existing models can only deal with isolated phenomena (e.g., garden paths) on small, specifically selected data sets. The challenge is to build models that integrate multiple aspects of human lan-guage processing at the syntactic, seman-tic, and discourse level. Like human lan-guage processing, these models should be incremental, predictive, broad coverage, and robust to noise. This challenge can only be met if standardized data sets and evaluation measures are developed.