The Plausibility of Sampling as an Algorithmic Theory of Sentence Processing

Dados Bibliográficos

ID22157809
AutoresJacob Louis Hoover (0000-0001-8651-5395, Mila - Quebec Artificial Intelligence Institute), Morgan Sonderegger (0000-0001-7675-2370, McGill University), Steven T Piantadosi (0000-0001-5499-4168, Berkeley College), Timothy J O’donnell (0000-0003-0291-8838, Canadian Institute for Advanced Research)
Ano2023
Volume7
Páginas1-42
Data de publicação2023-06-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoOpen MIND (JOURNAL)
Identificadores do periódicoISSN: 2470-2986 • E-ISSN: 2470-2986
EditoraMIT Press (PUBLISHER • US)
DOI10.1162/opmi_a_00086
PMID37637302
OpenAlexW4381687028
IdiomaEN
Citações recebidas8
Referências citadas40

Words that are more surprising given context take longer to process. However, no incremental parsing algorithm has been shown to directly predict this phenomenon. In this work, we focus on a class of algorithms whose runtime does naturally scale in surprisal—those that involve repeatedly sampling from the prior. Our first contribution is to show that simple examples of such algorithms predict runtime to increase superlinearly with surprisal, and also predict variance in runtime to increase. These two predictions stand in contrast with literature on surprisal theory [Hale, 2001; Levy, 2008a], which assumes that the expected processing cost increases linearly with surprisal, and makes no prediction about variance. In the second part of this paper, we conduct an empirical study of the relationship between surprisal and reading time, using a collection of modern language models to estimate surprisal. We find that with better language models, reading time increases superlinearly in surprisal, and also that variance increases. These results are consistent with the predictions of sampling-based algorithms

Grammar · Grammaticality · Linguistics · Natural language processing · Sentence · Computer Science · Natural Language Processing Techniques · Neurobiology of Language and Bilingualism · Syntax, Semantics, Linguistic Variation · Artificial Intelligence