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On Factors Influencing Typing Time: Insights from a Viral Online Typing Game
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In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021)
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Cloze Distillation: Improving Neural Language Models with Human Next-Word Prediction
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In: Association for Computational Linguistics (2021)
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Cloze Distillation: Improving Neural Language Models with Human Next-Word Prediction
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In: Association for Computational Linguistics (2021)
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Abstract:
Contemporary autoregressive language models (LMs) trained purely on corpus data have been shown to capture numerous features of human incremental processing. However, past work has also suggested dissociations between corpus probabilities and human next-word predictions. Here we evaluate several state-of-theart language models for their match to human next-word predictions and to reading time behavior from eye movements. We then propose a novel method for distilling the linguistic information implicit in human linguistic predictions into pre-trained LMs: Cloze Distillation. We apply this method to a baseline neural LM and show potential improvement in reading time prediction and generalization to held-out human cloze data.
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URL: https://hdl.handle.net/1721.1/138277.2
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On Factors Influencing Typing Time: Analyzing TypeRacer’s Massive Open Access Dataset ...
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Fourth Replication of Saffran, Newport, & Aslin (1996) Word segmentation: The role of distributional cues, Exp. 1 ...
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