Syntactic dependency length shaped by strategic memory allocation

Result code in 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>

Result on the web

<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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Result language

angličtina

Original language name

Syntactic dependency length shaped by strategic memory allocation

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

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Continuities

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Publication year

2024

Confidentiality

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

Article name in the collection

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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Number of pages

9

Pages from-to

1-9

Publisher name

Association for Computational Linguistics

Place of publication

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Event location

St. Julian's, Malta

Event date

Jan 1, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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