Automatically Neutralizing Subjective Bias in Text

Abstract: Texts like news, encyclopedias, and some social media strive for objectivity. Yet bias in the form of inappropriate subjectivity - introducing attitudes via framing, presupposing truth, and casting doubt - remains ubiquitous. This kind of bias erodes our collective trust and fuels social conflict. To address this issue, we introduce a novel testbed for natural language generation: automatically bringing inappropriately subjective text into a neutral point of view ("neutralizing" biased text). We also offer the first parallel corpus of biased language. The corpus contains 180,000 sentence pairs and originates from Wikipedia edits that removed various framings, presuppositions, and attitudes from biased sentences. Last, we propose two strong encoder-decoder baselines for the task. A straightforward yet opaque CONCURRENT system uses a BERT encoder to identify subjective words as part of the generation process. An interpretable and controllable MODULAR algorithm separates these steps, using (1) a BERT-based classifier to identify problematic words and (2) a novel join embedding through which the classifier can edit the hidden states of the encoder. Large-scale human evaluation across four domains (encyclopedias, news headlines, books, and political speeches) suggests that these algorithms are a first step towards the automatic identification and reduction of bias.

• The paper introduces the Wiki Neutrality Corpus and formulates text neutralization as a sequence-to-sequence transformation to remove subjective bias.
• It proposes two baseline models—a modular system using BERT and LSTM for detection and editing, and a concurrent system for direct generation—balancing interpretability and fluency.
• Comprehensive human evaluations and quantitative metrics show effective bias reduction, though further work is needed to improve fluency and meaning preservation.

An Analytical Overview of "Automatically Neutralizing Subjective Bias in Text"

The paper "Automatically Neutralizing Subjective Bias in Text" addresses the pervasive issue of subjective bias in various forms of written communication. The work focuses on natural language generation techniques to transform inappropriately subjective texts into more neutral renditions. The paper proposes a novel testbed and develops a new approach to tackle this bias, especially prominent in texts such as encyclopedias, news articles, and social media posts.

Key Contributions

The paper introduces the Wiki Neutrality Corpus (WNC), a substantial parallel corpus consisting of 180,000 biased and neutralized sentence pairs harvested from Wikipedia edits. This dataset serves as a foundation for advancing automated methods to reduce subjectivity bias. The corpus is notable for its scale and specificity, marking the first of its kind dedicated to biased language.

The research effort defines and constructs the task of text neutralization, setting it apart from previous endeavors focused on debiasing text representations like word embeddings. The task is conceptualized as a sequence-to-sequence transformation problem where the aim is to maintain semantic fidelity while eliminating subjective bias.

Two baseline models are proposed for this neutralization task: a modular algorithm and a concurrent system. The modular approach separates detection and editing processes, thereby allowing interpretability and controllability through a BERT-based detection module and an LSTM-based editing module. The concurrent system directly generates neutral text, providing simplicity at the cost of reduced interpretability.

Experimental Evaluation

The authors perform comprehensive evaluations with both quantitative and qualitative metrics. They conducted large-scale human evaluations, underscoring their algorithms' initial success in identifying and neutralizing bias across domains like encyclopedias, news headlines, books, and political speeches. The modular and concurrent systems effectively reduced bias according to human raters, with a notable finding that the performance on fluency and meaning preservation still leaves room for improvement compared to direct editorial efforts.

From a quantitative standpoint, the systems exhibited variances in metrics such as BLEU and accuracy. The modular system, due to its structured approach, presented an advantage in terms of fine-tuning bias reduction efforts, while the concurrent system maintained fluency and meaning preservation more effectively.

Implications and Future Directions

This work offers foundational steps towards automating the neutralization of subjective bias in text, a task critical in enhancing the objectivity of information consumed globally. The implications are significant for automated content moderation, journalistic endeavors, and educational resources, where maintaining a neutral tone is paramount. Moreover, it invites further exploration into more complex forms of bias involving multi-word constructs and cross-sentence dependencies that capture the nuanced nature of human subjectivity.

The research opens further opportunities to integrate this task with related areas such as automatic fact-checking, where verifying the factual basis of claims complements the neutralization process. The modular approach, with its ability to incorporate human oversight, suggests pathways for human-in-the-loop systems that can balance precision with editorial nuances.

In conclusion, this paper advocates for continued refinement and expansion of such methodologies, aiming for robust solutions that can seamlessly integrate into real-world applications, thereby promoting more objective and reliable textual information dissemination.