2025 NAACL NAACL 2025

Capturing Online SRC/ORC Effort with Memory Measures from a Minimalist Parser

Abstract

AbstractA parser for Minimalist grammars (Stabler, 2013) has been shown to successfully model sentence processing preferences across an array of languages and phenomena when combined with complexity metrics that relate parsing behavior to memory usage (Gerth, 2015; Graf et al., 2017; De Santo, 2020, a.o.). This model provides a quantifiable theory of the effects of fine-grained grammatical structure on cognitive cost, and can help strengthen the link between generative syntactic theory and sentence processing.However, work on it has focused on offline asymmetries.Here, we extend this approach by showing how memory-based measures of effort that explicitly consider minimalist-like structure-building operations improve our ability to account for word-by-word (online) behavioral data.

🌉 Interdisciplinary Bridge — Interdisciplinary and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — minimalist parser
🐝 Cross-Pollinator — Artificial Intelligence, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing