One Word, Two Sides: Traces of Stance in Contextualized Word Representations

Aina Garí Soler; Matthieu Labeau; Chloé Clavel

One Word, Two Sides: Traces of Stance in Contextualized Word Representations

Aina Garí Soler
, Matthieu Labeau
, Chloé Clavel

Institut Polytechnique de Paris

Résultats de recherche: Contribution à un journal › Article de conférence › Revue par des pairs

Résumé

The way we use words is influenced by our opinion. We investigate whether this is reflected in contextualized word embeddings. For example, is the representation of “animal” different between people who would abolish zoos and those who would not? We explore this question from a Lexical Semantic Change standpoint. Our experiments with BERT embeddings derived from datasets with stance annotations reveal small but significant differences in word representations between opposing stances.

langue originale	Anglais
Pages (de - à)	3950-3959
Nombre de pages	10
journal	Proceedings - International Conference on Computational Linguistics, COLING
Volume	29
Numéro de publication	1
état	Publié - 1 janv. 2022
Evénement	29th International Conference on Computational Linguistics, COLING 2022 - Hybrid, Gyeongju, Corée du Sud Durée: 12 oct. 2022 → 17 oct. 2022

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title = "One Word, Two Sides: Traces of Stance in Contextualized Word Representations",

abstract = "The way we use words is influenced by our opinion. We investigate whether this is reflected in contextualized word embeddings. For example, is the representation of “animal” different between people who would abolish zoos and those who would not? We explore this question from a Lexical Semantic Change standpoint. Our experiments with BERT embeddings derived from datasets with stance annotations reveal small but significant differences in word representations between opposing stances.",

author = "Soler, \{Aina Gar{\'i}\} and Matthieu Labeau and Chlo{\'e} Clavel",

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AU - Soler, Aina Garí

AU - Labeau, Matthieu

AU - Clavel, Chloé

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N2 - The way we use words is influenced by our opinion. We investigate whether this is reflected in contextualized word embeddings. For example, is the representation of “animal” different between people who would abolish zoos and those who would not? We explore this question from a Lexical Semantic Change standpoint. Our experiments with BERT embeddings derived from datasets with stance annotations reveal small but significant differences in word representations between opposing stances.

AB - The way we use words is influenced by our opinion. We investigate whether this is reflected in contextualized word embeddings. For example, is the representation of “animal” different between people who would abolish zoos and those who would not? We explore this question from a Lexical Semantic Change standpoint. Our experiments with BERT embeddings derived from datasets with stance annotations reveal small but significant differences in word representations between opposing stances.

UR - https://www.scopus.com/pages/publications/85153472244

M3 - Conference article

AN - SCOPUS:85153472244

SN - 2951-2093

VL - 29

SP - 3950

EP - 3959

JO - Proceedings - International Conference on Computational Linguistics, COLING

JF - Proceedings - International Conference on Computational Linguistics, COLING

IS - 1

Y2 - 12 October 2022 through 17 October 2022

ER -

One Word, Two Sides: Traces of Stance in Contextualized Word Representations

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