Syntactic dependency length shaped by strategic memory allocation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AWR7Q32KN" target="_blank" >RIV/00216208:11320/25:WR7Q32KN - isvavai.cz</a>

Výsledek na webu

<a href="https://aclanthology.org/2024.sigtyp-1.1" target="_blank" >https://aclanthology.org/2024.sigtyp-1.1</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Syntactic dependency length shaped by strategic memory allocation

Popis výsledku v původním jazyce

Human processing of nonlocal syntactic dependencies requires the engagement of limited working memory for encoding, maintenance, and retrieval. This process creates an evolutionary pressure for language to be structured in a way that keeps the subparts of a dependency closer to each other, an efficiency principle termed dependency locality. The current study proposes that such a dependency locality pressure can be modulated by the surprisal of the antecedent, defined as the first part of a dependency, due to strategic allocation of working memory. In particular, antecedents with novel and unpredictable information are prioritized for memory encoding, receiving more robust representation against memory interference and decay, and thus are more capable of handling longer dependency length. We examine this claim by analyzing dependency corpora of 11 languages, with word surprisal generated from GPT-3 language model. In support of our hypothesis, we find evidence for a positive correlation between dependency length and the antecedent surprisal in most of the languages in our analyses. A closer look into the dependencies with core arguments shows that this correlation consistently holds for subject relations but not for object relations.

Název v anglickém jazyce

Syntactic dependency length shaped by strategic memory allocation

Popis výsledku anglicky

Human processing of nonlocal syntactic dependencies requires the engagement of limited working memory for encoding, maintenance, and retrieval. This process creates an evolutionary pressure for language to be structured in a way that keeps the subparts of a dependency closer to each other, an efficiency principle termed dependency locality. The current study proposes that such a dependency locality pressure can be modulated by the surprisal of the antecedent, defined as the first part of a dependency, due to strategic allocation of working memory. In particular, antecedents with novel and unpredictable information are prioritized for memory encoding, receiving more robust representation against memory interference and decay, and thus are more capable of handling longer dependency length. We examine this claim by analyzing dependency corpora of 11 languages, with word surprisal generated from GPT-3 language model. In support of our hypothesis, we find evidence for a positive correlation between dependency length and the antecedent surprisal in most of the languages in our analyses. A closer look into the dependencies with core arguments shows that this correlation consistently holds for subject relations but not for object relations.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

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Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 6th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

ISBN

979-8-89176-071-4

ISSN

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e-ISSN

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Počet stran výsledku

9

Strana od-do

1-9

Název nakladatele

Association for Computational Linguistics

Místo vydání

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Místo konání akce

St. Julian's, Malta

Datum konání akce

1. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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