Are Transformers a Modern Version of ELIZA? Observations on French Object Verb Agreement

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10442255" target="_blank" >RIV/00216208:11320/21:10442255 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Are Transformers a Modern Version of ELIZA? Observations on French Object Verb Agreement

Original language description

Many recent works have demonstrated that unsupervised sentence representations of neural networks encode syntactic information by observing that neural language models are able to predict the agreement between a verb and its subject. We take a critical look at this line of research by showing that it is possible to achieve high accuracy on this agreement task with simple surface heuristics, indicating a possible flaw in our assessment of neural networks&apos; syntactic ability. Our fine-grained analyses of results on the long-range French object-verb agreement show that contrary to LSTMs, Transformers are able to capture a non-trivial amount of grammatical structure.

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

2021

Confidentiality

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

Article name in the collection

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

ISBN

978-1-955917-09-4

ISSN

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e-ISSN

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Number of pages

12

Pages from-to

4599-4610

Publisher name

Association for Computational Linguistics

Place of publication

Stroudsburg

Event location

Punta Cana

Event date

Nov 7, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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