Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims (1906.03538v1)

Abstract: One key consequence of the information revolution is a significant increase and a contamination of our information supply. The practice of fact checking won't suffice to eliminate the biases in text data we observe, as the degree of factuality alone does not determine whether biases exist in the spectrum of opinions visible to us. To better understand controversial issues, one needs to view them from a diverse yet comprehensive set of perspectives. For example, there are many ways to respond to a claim such as "animals should have lawful rights", and these responses form a spectrum of perspectives, each with a stance relative to this claim and, ideally, with evidence supporting it. Inherently, this is a natural language understanding task, and we propose to address it as such. Specifically, we propose the task of substantiated perspective discovery where, given a claim, a system is expected to discover a diverse set of well-corroborated perspectives that take a stance with respect to the claim. Each perspective should be substantiated by evidence paragraphs which summarize pertinent results and facts. We construct PERSPECTRUM, a dataset of claims, perspectives and evidence, making use of online debate websites to create the initial data collection, and augmenting it using search engines in order to expand and diversify our dataset. We use crowd-sourcing to filter out noise and ensure high-quality data. Our dataset contains 1k claims, accompanied with pools of 10k and 8k perspective sentences and evidence paragraphs, respectively. We provide a thorough analysis of the dataset to highlight key underlying language understanding challenges, and show that human baselines across multiple subtasks far outperform ma-chine baselines built upon state-of-the-art NLP techniques. This poses a challenge and opportunity for the NLP community to address.

• The paper proposes a framework and dataset for substantiated perspective discovery, the task of identifying diverse, evidence-supported perspectives about claims.
• An analysis using the new dataset reveals that existing NLP models struggle significantly with nuanced perspective discovery tasks compared to human performance.
• This work has practical implications for improving information systems by providing users with access to diverse, evidence-backed viewpoints to counter bias and limited information.

Diverse Perspectives Discovery in Natural Language Processing

In the paper titled "Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims," the authors propose a framework and dataset for addressing what they term substantiated perspective discovery. This task, which resides at the intersection of natural language understanding and computational argumentation, aims to identify a diverse set of perspectives—each supported by evidence—pertaining to a given claim.

Overview

The motivation underlying this research stems from the growing prevalence of biased information due to the limited perspective visibility offered by traditional search engines and fact-checking methodologies. Despite efforts in fact-verification, biases persist in the manner opinions are represented. Thus, an ability to discern diverse perspectives is critical for high-stakes applications such as media analysis, policymaking, and public discussion on controversial topics.

Methodology

The authors construct a dataset consisting of approximately 1,000 claims, with additional pools of 10,000 perspectives and 8,000 evidence paragraphs. These data were sourced initially from online debate platforms and further augmented using web data, leveraging search engines for enhanced diversity. A rigorous crowdsourcing procedure ensures data quality by filtering out noise and validating pertinent attributes such as stance and relevance.

The key tasks introduced involve:

1. Stance Classification: Determining whether a perspective supports or opposes the claim.
2. Perspective Extraction: Identifying relevant perspectives from a larger pool, necessitating semantic understanding to distinguish unique viewpoints or degree equivalents.
3. Evidence Association: Validating perspectives with supporting evidence gleaned from the textual corpus.

Results

An analysis of the designed dataset shows formidable challenges. Human baseline performance significantly surpassed that of machine learning baselines built on state-of-the-art NLP methodologies, including BERT, indicating existing models' inadequacies in comprehending nuanced argumentation and semantic subtleties inherent in perspective identification.

Implications

The conspicuous gap between human and machine performance underlined in this paper suggests numerous potential research avenues in NLP. The sophistication required to address perspective discovery effectively embodies natural language understanding at deeper semantic levels than typically engaged.

Practically, this work opens opportunities for the development of systems aiding in media literacy and bias mitigation. By incorporating robust substantiated perspective discovery into mainstream information retrieval systems, users can enjoy a broadened horizon of opinions particularly crucial in an era of polarized discourse.

Future Work

Looking forward, the integration of trustworthiness assessment and credibility evaluation remains a natural extension to this framework. In addition, automating the argumentative feature extraction from claims in natural language remains a challenge. These steps are essential to deploy perspective discovery systems effectively in real-world applications.

In conclusion, the authors' work provides a valuable benchmark and methodology for exploring substantiated perspectives in NLP, presenting both a challenge and opportunity for further advancement in automated discourse analysis and understanding. This research is poised to serve as a cornerstone for subsequent developments in handling biased and limited information by fostering pluralistic digital dialogues.