Identifying Implicit Targets in Hate Speech: A Novel Dataset and Baseline Model

Khái niệm cốt lõi

This study introduces a new task called Implicit Target Span Identification (iTSI) that aims to detect both explicit and implicit references to target groups in hate speech content. The authors create a novel dataset called Implicit-Target-Span (ITS) by leveraging a pooling-based annotation approach to capture a diverse set of implicit and explicit target spans. They also establish a baseline model, TargetDetect, using sequence tagging techniques to identify target spans in the ITS dataset.

Tóm tắt

The key highlights and insights from the content are:

The authors define a new task called Implicit Target Span Identification (iTSI) that requires models to identify token spans within content that target protected groups, even when the targets are not explicitly stated.

To support research in this area, the authors create a new dataset called Implicit-Target-Span (ITS) by leveraging a pooling-based annotation approach. This allows them to capture both implicit and explicit references to target groups across three existing hate speech datasets: IHC, DynaHate, and SBIC.

The ITS dataset contains 57k annotated samples with an average of 1.7 target spans per sample. The authors find that the ITS dataset has nearly 20 times more unique targets compared to the original datasets, highlighting the prevalence of implicit targets.

The authors establish a baseline model called TargetDetect that uses a sequence tagging approach with transformer-based encoders like BERT and RoBERTa to identify target spans in the ITS dataset.

Experiments show that the RoBERTa-Large encoder achieves the best performance, with an F1 score of up to 80.8% on the ITS test sets. The model also performs competitively on the PLEAD dataset, a publicly available hate speech dataset.

Error analysis reveals common failure modes of the baseline model, such as boundary errors, discrepancies in the number of predicted and ground truth spans, and challenges with obfuscated, implicit, and subtle target references.

Overall, this work introduces a novel task and dataset to advance research on identifying implicit targets in hate speech, and provides a strong baseline model to build upon for future work.

Thống kê

"Songwriters don't belong and never will, so let's just remove the piano brains from this place!"
"European people"

Trích dẫn

None

Thông tin chi tiết chính được chắt lọc từ

Target Span Detection for Implicit Harmful Content

by Nazanin Jafa... lúc arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.19836.pdf

Yêu cầu sâu hơn

How can the pooling-based annotation approach be further improved to ensure higher-quality and more consistent target span annotations?

The pooling-based annotation approach can be enhanced in several ways to elevate the quality and consistency of target span annotations. Firstly, incorporating a larger and more diverse pool of human annotators can help capture a broader range of perspectives and nuances in identifying implicit target spans. This can mitigate individual biases and errors, leading to more comprehensive annotations. Additionally, implementing a robust adjudication process for conflicting annotations among human annotators can help resolve discrepancies and ensure accurate annotations.
Furthermore, refining the prompts used in conjunction with Large Language Models (LLMs) can significantly impact the quality of annotations. Developing more nuanced and context-specific prompts tailored to the intricacies of implicit hate speech can guide annotators and LLMs to identify target spans more effectively. Continuous feedback loops between human annotators and LLMs can also refine the annotation process over time, improving the overall quality and consistency of target span annotations.

How can the insights from this work on identifying implicit targets be extended to other domains beyond hate speech, where subtle references to entities or concepts may be important to detect?

The insights gained from identifying implicit targets in hate speech can be extrapolated to various other domains where detecting subtle references to entities or concepts is crucial. One potential application is in sentiment analysis, where understanding implicit references to sentiments or emotions in text is essential for accurate analysis. By adapting the methodology developed for hate speech detection to sentiment analysis, researchers can uncover implicit sentiments expressed in text, leading to more nuanced and insightful analyses.
Moreover, in the field of marketing and consumer behavior analysis, identifying implicit references to brands, products, or services can provide valuable insights into consumer preferences and trends. By leveraging the techniques and models used in detecting implicit targets in hate speech, researchers can develop tools to extract implicit references in consumer feedback, social media posts, and reviews, enhancing market research and strategic decision-making.
Additionally, in legal and compliance domains, detecting implicit references to legal terms, regulations, or compliance issues can aid in monitoring and ensuring adherence to legal standards. By applying the learnings from identifying implicit targets in hate speech, researchers can develop systems to analyze legal documents, contracts, and communications for implicit references, facilitating compliance management and risk assessment.

How can the insights from this work on identifying implicit targets be extended to other domains beyond hate speech, where subtle references to entities or concepts may be important to detect?

The insights gained from identifying implicit targets in hate speech can be extrapolated to various other domains where detecting subtle references to entities or concepts is crucial. One potential application is in sentiment analysis, where understanding implicit references to sentiments or emotions in text is essential for accurate analysis. By adapting the methodology developed for hate speech detection to sentiment analysis, researchers can uncover implicit sentiments expressed in text, leading to more nuanced and insightful analyses.
Moreover, in the field of marketing and consumer behavior analysis, identifying implicit references to brands, products, or services can provide valuable insights into consumer preferences and trends. By leveraging the techniques and models used in detecting implicit targets in hate speech, researchers can develop tools to extract implicit references in consumer feedback, social media posts, and reviews, enhancing market research and strategic decision-making.
Additionally, in legal and compliance domains, detecting implicit references to legal terms, regulations, or compliance issues can aid in monitoring and ensuring adherence to legal standards. By applying the learnings from identifying implicit targets in hate speech, researchers can develop systems to analyze legal documents, contracts, and communications for implicit references, facilitating compliance management and risk assessment.