2025
CONLL
CoNLL 2025
What does memory retrieval leave on the table? Modelling the Cost of Semi-Compositionality with MINERVA2 and sBERT
Abstract
AbstractDespite being ubiquitous in natural language, collocations (e.g., kick+habit) incur a unique processing cost, compared to compositional phrases (kick+door) and idioms (kick+bucket). We confirm this cost with behavioural data as well as MINERVA2, a memory model, suggesting that collocations constitute a distinct linguistic category. While the model fails to fully capture the observed human processing patterns, we find that below a specific item frequency threshold, the model’s retrieval failures align with human reaction times across conditions. This suggests an alternative processing mechanism that activates when memory retrieval fails.
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The Questioner
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Interdisciplinary Bridge
— Machine Learning and Natural Language Processing
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Keyword Pioneer
— collocation processing
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio