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New dataset STRUCTURALCOST models human sentence processing difficulty

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced STRUCTURALCOST, a new dataset designed to measure human sentence processing difficulty, particularly focusing on long-distance subject-verb dependency resolution. The dataset, comprising 475 participants and 40,800 observations, reveals that human reading times increase with dependency length, influenced by syntactic embedding rather than just linear distance. While various language models, including n-gram, SSM, and transformer architectures, partially replicate this difficulty profile, they underestimate the integration cost experienced by humans, suggesting they capture predictive aspects but not the full working memory integration cost. AI

IMPACT Provides data to better evaluate the cognitive plausibility of language models and improve their understanding of human sentence processing.

RANK_REASON The cluster contains an academic paper detailing a new dataset for NLP research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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New dataset STRUCTURALCOST models human sentence processing difficulty

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The cluster contains an academic paper detailing a new dataset for NLP research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Nina Nusbaumer, Iria de-Dios-Flores, Corentin Bel, Christophe Pallier, Guillaume Wisniewski, Beno\^it Crabb\'e ·

    STRUCTURALCOST: A controlled reading time dataset for modeling human sentence processing difficulty

    arXiv:2610.08208v1 Announce Type: cross Abstract: We introduce STRUCTURALCOST, a self-paced reading dataset of 475 participants and 40,800 observations isolating the processing cost of long-distance subject-verb dependency resolution. We replicate a low-powered psycholinguistic f…

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