A Latent-Variable Model for Intrinsic Probing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A8YZAZEJZ" target="_blank" >RIV/00216208:11320/23:8YZAZEJZ - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167964610&partnerID=40&md5=412f7f84d56ccb15ddff0b3e37b68d5f" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167964610&partnerID=40&md5=412f7f84d56ccb15ddff0b3e37b68d5f</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

A Latent-Variable Model for Intrinsic Probing

Popis výsledku v původním jazyce

"The success of pre-trained contextualized representations has prompted researchers to analyze them for the presence of linguistic information. Indeed, it is natural to assume that these pre-trained representations do encode some level of linguistic knowledge as they have brought about large empirical improvements on a wide variety of NLP tasks, which suggests they are learning true linguistic generalization. In this work, we focus on intrinsic probing, an analysis technique where the goal is not only to identify whether a representation encodes a linguistic attribute but also to pinpoint where this attribute is encoded. We propose a novel latent-variable formulation for constructing intrinsic probes and derive a tractable variational approximation to the log-likelihood. Our results show that our model is versatile and yields tighter mutual information estimates than two intrinsic probes previously proposed in the literature. Finally, we find empirical evidence that pre-trained representations develop a cross-lingually entangled notion of morphosyntax. Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved."

Název v anglickém jazyce

A Latent-Variable Model for Intrinsic Probing

Popis výsledku anglicky

"The success of pre-trained contextualized representations has prompted researchers to analyze them for the presence of linguistic information. Indeed, it is natural to assume that these pre-trained representations do encode some level of linguistic knowledge as they have brought about large empirical improvements on a wide variety of NLP tasks, which suggests they are learning true linguistic generalization. In this work, we focus on intrinsic probing, an analysis technique where the goal is not only to identify whether a representation encodes a linguistic attribute but also to pinpoint where this attribute is encoded. We propose a novel latent-variable formulation for constructing intrinsic probes and derive a tractable variational approximation to the log-likelihood. Our results show that our model is versatile and yields tighter mutual information estimates than two intrinsic probes previously proposed in the literature. Finally, we find empirical evidence that pre-trained representations develop a cross-lingually entangled notion of morphosyntax. Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved."

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

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

"Proc. AAAI Conf. Artif. Intell., AAAI"

ISBN

978-157735880-0

ISSN

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e-ISSN

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Počet stran výsledku

9

Strana od-do

13591-13599

Název nakladatele

AAAI Press

Místo vydání

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Místo konání akce

Melaka, Malaysia

Datum konání akce

1. 1. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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