2024 EMNLP EMNLP 2024

Lossy Context Surprisal Predicts Task-Dependent Patterns in Relative Clause Processing

Abstract

AbstractEnglish relative clauses are a critical test case for theories of syntactic processing. Expectation- and memory-based accounts make opposing predictions, and behavioral experiments have found mixed results. We present a technical extension of Lossy Context Surprisal (LCS) and use it to model relative clause processing in three behavioral experiments. LCS predicts key results at distinct retention rates, showing that task-dependent memory demands can account for discrepant behavioral patterns in the literature.

🌉 Interdisciplinary Bridge — Interdisciplinary and Natural Language Processing
🧭 Keyword Pioneer — memory-based processing
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing