2006 NIPS NeurIPS 2006

Speakers optimize information density through syntactic reduction

Abstract

If language users are rational, they might choose to structure their utterances so as to optimize communicative properties. In particular, information-theoretic and psycholinguistic considerations suggest that this may include maximizing the uniformity of information density in an utterance. We investigate this possibility in the context of syntactic reduction, where the speaker has the option of either marking a higher-order unit (a phrase) with an extra word, or leaving it unmarked. We demonstrate that speakers are more likely to reduce less information-dense phrases. In a second step, we combine a stochastic model of structured utterance production with a logistic-regression model of syntactic reduction to study which types of cues speakers employ when estimating the predictability of upcoming elements. We demonstrate that the trend toward predictability-sensitive syntactic reduction (Jaeger, 2006) is robust in the face of a wide variety of control variables, and present evidence that speakers use both surface and structural cues for predictability estimation.

🚀 Conference Pioneer — NIPS 2006
🌉 Interdisciplinary Bridge — Interdisciplinary and Natural Language Processing
📈 Trend Setter — Syntax
🧭 Keyword Pioneer — syntactic reduction
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics
🐣 Hot Topic Early Bird — information theory