The Plausibility of Sampling as an Algorithmic Theory of Sentence Processing

Datos 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)
Año2023
Volumen7
Páginas1-42
Fecha de publicación2023-06-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaOpen MIND (JOURNAL)
Identificadores de la revistaISSN: 2470-2986 • E-ISSN: 2470-2986
EditorialMIT Press (PUBLISHER • US)
DOI10.1162/opmi_a_00086
PMID37637302
OpenAlexW4381687028
IdiomaEN
Citas recibidas8
Referencias 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