Unlocking Bias Detection: Leveraging Transformer-Based Models for Content Analysis
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866916209066246144 |
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| author | Raza, Shaina Bamgbose, Oluwanifemi Chatrath, Veronica Ghuge, Shardul Sidyakin, Yan Muaad, Abdullah Y |
| author_facet | Raza, Shaina Bamgbose, Oluwanifemi Chatrath, Veronica Ghuge, Shardul Sidyakin, Yan Muaad, Abdullah Y |
| contents | Bias detection in text is crucial for combating the spread of negative stereotypes, misinformation, and biased decision-making. Traditional language models frequently face challenges in generalizing beyond their training data and are typically designed for a single task, often focusing on bias detection at the sentence level. To address this, we present the Contextualized Bi-Directional Dual Transformer (CBDT) \textcolor{green}{\faLeaf} classifier. This model combines two complementary transformer networks: the Context Transformer and the Entity Transformer, with a focus on improving bias detection capabilities. We have prepared a dataset specifically for training these models to identify and locate biases in texts. Our evaluations across various datasets demonstrate CBDT \textcolor{green} effectiveness in distinguishing biased narratives from neutral ones and identifying specific biased terms. This work paves the way for applying the CBDT \textcolor{green} model in various linguistic and cultural contexts, enhancing its utility in bias detection efforts. We also make the annotated dataset available for research purposes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_00347 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Unlocking Bias Detection: Leveraging Transformer-Based Models for Content Analysis Raza, Shaina Bamgbose, Oluwanifemi Chatrath, Veronica Ghuge, Shardul Sidyakin, Yan Muaad, Abdullah Y Computation and Language Artificial Intelligence Bias detection in text is crucial for combating the spread of negative stereotypes, misinformation, and biased decision-making. Traditional language models frequently face challenges in generalizing beyond their training data and are typically designed for a single task, often focusing on bias detection at the sentence level. To address this, we present the Contextualized Bi-Directional Dual Transformer (CBDT) \textcolor{green}{\faLeaf} classifier. This model combines two complementary transformer networks: the Context Transformer and the Entity Transformer, with a focus on improving bias detection capabilities. We have prepared a dataset specifically for training these models to identify and locate biases in texts. Our evaluations across various datasets demonstrate CBDT \textcolor{green} effectiveness in distinguishing biased narratives from neutral ones and identifying specific biased terms. This work paves the way for applying the CBDT \textcolor{green} model in various linguistic and cultural contexts, enhancing its utility in bias detection efforts. We also make the annotated dataset available for research purposes. |
| title | Unlocking Bias Detection: Leveraging Transformer-Based Models for Content Analysis |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2310.00347 |