Challenge: a number of fact checking techniques are used to identify and eliminate biases in text data.
Approach: They propose to use search engines to expand and diversify a dataset of claims, perspectives and evidence to address a selection bias.
Outcome: The proposed approach outperforms existing methods in a language understanding task.

Similar Papers

PerspectroScope: A Window to the World of Diverse Perspectives (P19-3)

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Challenge: PerspectroScope is a web-based system that lets users query a discussion-worthy natural language claim .
Approach: They propose a web-based system which lets users query a discussion-worthy natural language claim and extract and visualize various perspectives in support or against the claim.
Outcome: The proposed system lets users query a discussion-worthy natural language claim and extract and visualize various perspectives in support or against the claim.
Seeing All Sides: Multi-Perspective In-Context Learning for Subjective NLP (2026.findings-eacl)

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Challenge: Modern language models excel at factual reasoning but struggle with value diversity, authors say . task-sensitive tasks such as hate speech expose this limitation . human disagreement captures the diversity of plausible human perspectives, authors argue .
Approach: They evaluate four large language models with human disagreements on five datasets . they find multi-perspective in-context learning outperforms standard prompting .
Outcome: The proposed approach outperforms standard prompting on English labels while disaggregated soft predictions better align with human judgments in Arabic and Italian datasets.
Towards Multi-Perspective NLP Systems: A Thesis Proposal (2025.acl-srw)

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Challenge: Existing approaches to resolving disagreements ignore individual opinions and can result in the marginalization of minority perspectives.
Approach: They propose to preserve individual labels in human-annotated datasets for subjective tasks and propose solutions for developing Perspective-Aware by design systems.
Outcome: The proposed framework will be used to develop more responsible and inclusive models.
Aligning Multidimensional Worldviews and Discovering Ideological Differences (2021.emnlp-main)

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Challenge: Existing work on understanding worldviews and ideological distinctions focuses on political polarization . et al., 2018: a novel method for uncovering complex ideological and worldview characteristics of communities.
Approach: They propose a method to uncover multifaceted ideological differences across multiple axes . they use comments from the largest communities on reddit.com to train word embedding models .
Outcome: The proposed method can uncover complex ideological differences across multiple axes of polarization using over 1B comments from the largest communities on reddit.com representing 40% of Reddit activity.
ClaimDiff: Comparing and Contrasting Claims on Contentious Issues (2023.findings-acl)

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Challenge: Using fact verification tasks, however, can not detect subtle differences in factually consistent claims, which might bias the readers.
Approach: They propose a novel dataset that primarily focuses on comparing the nuance between claim pairs.
Outcome: The proposed dataset shows that human-labeled 2,941 claim pairs are weaker than baselines, showing a 19% absolute gap with the baselines.
Open-World Evaluation for Retrieving Diverse Perspectives (2025.naacl-long)

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Challenge: Existing retrieval systems only cover diverse perspectives on 33.74% of the examples . existing systems only focus on relevance to the question, ignoring diversity.
Approach: They build a Benchmark for Retrieval Diversity for Subjective questions (BERDS) based on a question and diverse perspectives associated with the question . they evaluate retrievers paired with a corpus to determine whether each document contains a perspective .
Outcome: The proposed approach improves retrieval diversity on complex questions . existing retrieval systems only cover diverse perspectives on 33.74% of the examples .
PerspectiveMod: A Perspectivist Resource for Deliberative Moderation (2025.emnlp-main)

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Challenge: Human moderators in online discussions face a heterogeneous range of tasks that go beyond content moderation, or policing.
Approach: They propose a dataset of online comments annotated for the question "Does this comment require moderation?" they aim to improve discussion quality by analyzing annotator perspectives and annotating their views.
Outcome: The proposed model is unique in its intentional variation across the level of moderation experience embedded in the source data, the annotator profiles and the individuality of the annnotator.
Offer a Different Perspective: Modeling the Belief Alignment of Arguments in Multi-party Debates (2022.emnlp-main)

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Challenge: Existing work on persuasion in online forums focuses on identifying debate winners and winning negotiation games.
Approach: They adopt a hierarchical generative Variational Autoencoder model to model winning arguments . they propose competing hypotheses about the nature of argumentation .
Outcome: The proposed model predicts winning arguments in reddit debates . it uses a hierarchical generative Variational Autoencoder to model argumentation .
Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation (2024.findings-acl)

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Challenge: Recent advances in large language models (LLMs) have made it difficult to build an automated debate system that helps people to synthesise persuasive arguments.
Approach: They propose to use an argument mining dataset to capture the end-to-end process of preparing an argumentative essay for a debate.
Outcome: The proposed dataset shows that it performs better on individual tasks than on human-centred evaluations.
Proposal: From One-Fit-All to Perspective Aware Modeling (2025.acl-srw)

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Challenge: Variation in human annotation and human perspectives has drawn increasing attention in natural language processing research.
Approach: They propose to use annotation formats that better capture granularity and uncertainty of individual judgments and annotation modeling that leverages socio-demographic features to better represent and predict underrepresented or minority perspectives.
Outcome: The proposed tasks aim to advance natural language processing research towards more faithfully reflecting the diversity of human interpretation, enhancing both inclusiveness and fairness in language technologies.

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