Speakers optimize information density through syntactic reduction

By Roger Levy, T. Florian Jaeger. Neural Information Processing Systems, 2006

521 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/bec3b18d0b74b7154882505545265b471bd7e68f

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In Artificial Intelligence

Citations in the corpus

The communicative function of ambiguity in language. (2011, unknown): “…used to explain phenomena such as discourse-level predictability (Genzel & Charniak, 2002, 2003; Piantadosi & Gibson, 2008; Qian & Jaeger, submitted), syntactic choice (Jaeger, 2006; Levy & Jaeger, 2007; Jaeger, 2010) and reduction (Van Son & Pols, 2003; Aylett & Turk, 2004; Frank & Jaeger, 2008).”
Experimental and theoretical advances in prosody: A review (2010, background): “More recent research by Jaeger ( 2006 ) and Levy and Jaeger ( 2007 ) shows that choices between different lexical and syntactic options (eg, the choice between inserting a relative pronoun or not) can be explained by this idea—by inserting a relative pronoun, the redundancy of the signal is increase”
Typical Decoding for Natural Language Generation (2022, background): “The uniform information density (UID) hypothesis (Fenk and Fenk, 1980; Levy and Jaeger, 2007) states that speakers construct their utterances such that information is distributed uniformly across them.”
Why discourse affects speakers’ choice of referring expressions (2015, methodology): “In addition, selecting between a pronoun and a more specified referring expression involves deciding how much information to convey, whereas previous applications of UID (Levy and Jaeger, 2007) have been concerned with deciding between different ways of expressing the same information content.”
The Goldilocks Effect: Human Infants Allocate Attention to Visual Sequences That Are Neither Too Simple Nor Too Complex (2012, background): “Similar hypotheses about how adults allocate their limited resources in the language domain–for example, those supporting a uniform information principle [33–37]–may suggest that what we have observed in infants reflects a ubiquitous constraint across domains and developmental levels.”
Probabilistic brains: knowns and unknowns (2013, background): “These experiments have shown that human behavior is highly consistent with probabilistic reasoning not only in the sensory domain 8–12 , but also in the motor 13–15 and cognitive 16–22 domains.”
Predictability Effects on Durations of Content and Function Words in Conversational English (2009, background): “Finally, it is possible to recast probabilistic reduction in terms of redundancy or informativeness, and to presume that the production process is governed by smoothing mechanisms that ensure the even distribution of informativeness over the time course of speech (Aylett, 1999; Levy & Jaeger, 2007;”