Beyond Explanation: A Case for Exploratory Text Visualizations of Non-Aggregated, Annotated Datasets
This paper presents an overview of text visualization techniques relevant for data perspectivism, aiming to facilitate analysis of annotated datasets for the datasets’ creators and stakeholders. Data perspectivism advocates for publishing non-aggregated, annotated text data, recognizing that for highly subjective tasks, such as bias detection and hate speech detection, disagreements among annotators may indicate conflicting yet equally valid interpretations of a text. While the publication of non-aggregated, annotated data makes different interpretations of text corpora available, barriers still exist to investigating patterns and outliers in annotations of the text. Techniques from text visualization can overcome these barriers, facilitating intuitive data analysis for NLP researchers and practitioners, as well as stakeholders in NLP systems, who may not have data science or computing skills. In this paper we discuss challenges with current dataset creation practices and annotation platforms, followed by a discussion of text visualization techniques that enable open-ended, multi-faceted, and iterative analysis of annotated data.
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Bias DetectionHate Speech DetectionvalidSimilar Papers 제목 키워드 기반
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