@inproceedings{srivastava-etal-2021-berts,
title = "What {BERT}s and {GPT}s know about your brand? Probing contextual language models for affect associations",
author = "Srivastava, Vivek and
Pilli, Stephen and
Bhat, Savita and
Pedanekar, Niranjan and
Karande, Shirish",
editor = "Agirre, Eneko and
Apidianaki, Marianna and
Vuli{\'c}, Ivan",
booktitle = "Proceedings of Deep Learning Inside Out (DeeLIO): The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.deelio-1.12",
doi = "10.18653/v1/2021.deelio-1.12",
pages = "119--128",
abstract = "Investigating brand perception is fundamental to marketing strategies. In this regard, brand image, defined by a set of attributes (Aaker, 1997), is recognized as a key element in indicating how a brand is perceived by various stakeholders such as consumers and competitors. Traditional approaches (e.g., surveys) to monitor brand perceptions are time-consuming and inefficient. In the era of digital marketing, both brand managers and consumers engage with a vast amount of digital marketing content. The exponential growth of digital content has propelled the emergence of pre-trained language models such as BERT and GPT as essential tools in solving myriads of challenges with textual data. This paper seeks to investigate the extent of brand perceptions (i.e., brand and image attribute associations) these language models encode. We believe that any kind of bias for a brand and attribute pair may influence customer-centric downstream tasks such as recommender systems, sentiment analysis, and question-answering, e.g., suggesting a specific brand consistently when queried for innovative products. We use synthetic data and real-life data and report comparison results for five contextual LMs, viz. BERT, RoBERTa, DistilBERT, ALBERT and BART.",
}
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%0 Conference Proceedings
%T What BERTs and GPTs know about your brand? Probing contextual language models for affect associations
%A Srivastava, Vivek
%A Pilli, Stephen
%A Bhat, Savita
%A Pedanekar, Niranjan
%A Karande, Shirish
%Y Agirre, Eneko
%Y Apidianaki, Marianna
%Y Vulić, Ivan
%S Proceedings of Deep Learning Inside Out (DeeLIO): The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F srivastava-etal-2021-berts
%X Investigating brand perception is fundamental to marketing strategies. In this regard, brand image, defined by a set of attributes (Aaker, 1997), is recognized as a key element in indicating how a brand is perceived by various stakeholders such as consumers and competitors. Traditional approaches (e.g., surveys) to monitor brand perceptions are time-consuming and inefficient. In the era of digital marketing, both brand managers and consumers engage with a vast amount of digital marketing content. The exponential growth of digital content has propelled the emergence of pre-trained language models such as BERT and GPT as essential tools in solving myriads of challenges with textual data. This paper seeks to investigate the extent of brand perceptions (i.e., brand and image attribute associations) these language models encode. We believe that any kind of bias for a brand and attribute pair may influence customer-centric downstream tasks such as recommender systems, sentiment analysis, and question-answering, e.g., suggesting a specific brand consistently when queried for innovative products. We use synthetic data and real-life data and report comparison results for five contextual LMs, viz. BERT, RoBERTa, DistilBERT, ALBERT and BART.
%R 10.18653/v1/2021.deelio-1.12
%U https://aclanthology.org/2021.deelio-1.12
%U https://doi.org/10.18653/v1/2021.deelio-1.12
%P 119-128
Markdown (Informal)
[What BERTs and GPTs know about your brand? Probing contextual language models for affect associations](https://aclanthology.org/2021.deelio-1.12) (Srivastava et al., DeeLIO 2021)
ACL