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Improving the Reading Rate of Double-R-Language



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Improving the Reading Rate of Double R Language Mary Freiman1 and Jerry Ball2 L3 Communications1 Air Force Research Laboratory2 Mary Freiman mesa afmc af mil Jerry Ball mesa afmc af mil pilot and determines when to communicate with the other teammates to acquire needed information A significant challenge for the reading comprehension abilities of the model is input variability A corpus of text chat communications that was collected in an experiment involving human subjects and the UAV simulation is full of variability in the form of linguistic input see Table 1 For competent readers misspelled words activate the intended lexical items because they contain many of the same letters and trigrams Perea Lupker 2003 Hence key requirements of the reading model include the ability to handle misspellings in input the ability to separate perceptually conjoined units e g separating punctuation from words as in He went but not etc separating words lacking spaces as in yougo for you go and the ability to recognize multi word expressions e g speed up and multi unit words e g a priori h area Abstract This paper describes changes to a model of reading comprehension to improve its reading rate and bring it into closer alignment with human reading rates The broader context of the research is development of language capable synthetic teammates that can be integrated into team training simulations To use synthetic teammates in team training without detriment we believe the synthetic teammates must be both functional and cognitively plausible By functional we mean that the synthetic teammate operates in real time performs the task and handles the range of linguistic inputs that are encountered By cognitively plausible we mean that the synthetic teammate adheres to well established cognitive constraints on human language processing including the incremental and interactive processing of language at human reading rates Achieving human reading rates in a cognitively plausible and



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