CONF-LA delivers a low-latency online method for line assignment in noisy gaze data that narrows the online-offline performance gap to 1-2% and reaches ~95% median accuracy on children's data.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Early layers of language models predict early-pass human reading times better than surprisal, with surprisal superior for late-pass measures and strong variation by language.
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Sure About That Line? Approaching Confidence-Based, Real-Time Line Assignment in Reading Gaze Data
CONF-LA delivers a low-latency online method for line assignment in noisy gaze data that narrows the online-offline performance gap to 1-2% and reaches ~95% median accuracy on children's data.
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Probing for Reading Times
Early layers of language models predict early-pass human reading times better than surprisal, with surprisal superior for late-pass measures and strong variation by language.