Varying the number of simultaneous parses in RNNGs increases predicted garden-path effects but does not fully reconcile LM surprisal with human reading times.
Agreement Attraction in
4 Pith papers cite this work. Polarity classification is still indexing.
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Machine interpreting should shift from fidelity metrics to three design priorities—agency, grounding, and experience—drawn from interpreting studies to close the usability gap with human-mediated communication.
LLM surprisal and attention entropy replicate syncretism modulation of agreement attraction in English and German, align with null results in Turkish, and partially match Russian patterns.
A multilingual AI interface maps text to contextually relevant pictograms with expert-rated semantic appropriateness above 90 percent across English, French, Italian, Spanish, and Arabic for reading rehabilitation.
citing papers explorer
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Why are language models less surprised than humans? Testing the Parse Multiplicity Mismatch Hypothesis
Varying the number of simultaneous parses in RNNGs increases predicted garden-path effects but does not fully reconcile LM surprisal with human reading times.
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Bridging the Usability Gap: Lessons from Interpreting Studies for Machine Interpreting Design
Machine interpreting should shift from fidelity metrics to three design priorities—agency, grounding, and experience—drawn from interpreting studies to close the usability gap with human-mediated communication.
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Quantifying the cross-linguistic effects of syncretism on agreement attraction
LLM surprisal and attention entropy replicate syncretism modulation of agreement attraction in English and German, align with null results in Turkish, and partially match Russian patterns.
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Robust Multilingual Text-to-Pictogram Mapping for Scalable Reading Rehabilitation
A multilingual AI interface maps text to contextually relevant pictograms with expert-rated semantic appropriateness above 90 percent across English, French, Italian, Spanish, and Arabic for reading rehabilitation.