CultureBERT: Measuring Corporate Culture With Transformer-Based Language Models

Fuente: arXiv
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Autores principales: Koch, Sebastian, Pasch, Stefan
Formato: Preprint
Publicado: 2022
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author Koch, Sebastian
Pasch, Stefan
author_facet Koch, Sebastian
Pasch, Stefan
contents This paper introduces transformer-based language models to the literature measuring corporate culture from text documents. We compile a unique data set of employee reviews that were labeled by human evaluators with respect to the information the reviews reveal about the firms' corporate culture. Using this data set, we fine-tune state-of-the-art transformer-based language models to perform the same classification task. In out-of-sample predictions, our language models classify 17 to 30 percentage points more of employee reviews in line with human evaluators than traditional approaches of text classification. We make our models publicly available.
format Preprint
id arxiv_https___arxiv_org_abs_2212_00509
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle CultureBERT: Measuring Corporate Culture With Transformer-Based Language Models
Koch, Sebastian
Pasch, Stefan
Computation and Language
This paper introduces transformer-based language models to the literature measuring corporate culture from text documents. We compile a unique data set of employee reviews that were labeled by human evaluators with respect to the information the reviews reveal about the firms' corporate culture. Using this data set, we fine-tune state-of-the-art transformer-based language models to perform the same classification task. In out-of-sample predictions, our language models classify 17 to 30 percentage points more of employee reviews in line with human evaluators than traditional approaches of text classification. We make our models publicly available.
title CultureBERT: Measuring Corporate Culture With Transformer-Based Language Models
topic Computation and Language
url https://arxiv.org/abs/2212.00509