# Consistency is Key: Disentangling Label Variation in Natural Language Processing with Intra-Annotator Agreement

arXiv: https://arxiv.org/abs/2301.10684 · HTML: https://mlsanity.com/paper/2301.10684/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 100 references that are in the map.
- First posted on arXiv 25 Jan 2023.

## Builds on (most cited first, 21 of 21)

- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [MultiVerS: Improving scientific claim verification with weak supervision and full-document context](https://mlsanity.com/paper/2112.01640/) (2021)
- [The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation](https://mlsanity.com/paper/2211.02570/) (2022)
- [Toward a Perspectivist Turn in Ground Truthing for Predictive Computing](https://mlsanity.com/paper/2109.04270/) (2021)
- [On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translation](https://mlsanity.com/paper/2005.03642/) (2020)
- [Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation](https://mlsanity.com/paper/1712.01765/) (2017)
- [Agreeing to Disagree: Annotating Offensive Language Datasets with Annotators' Disagreement](https://mlsanity.com/paper/2109.13563/) (2021)
- [Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement Learning](https://mlsanity.com/paper/1805.10627/) (2018)
- [Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing](https://mlsanity.com/paper/2104.10097/) (2021)
- [ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AI](https://mlsanity.com/paper/2109.09483/) (2021)
- [Theory-Grounded Measurement of U.S. Social Stereotypes in English Language Models](https://mlsanity.com/paper/2206.11684/) (2022)
- [Why Does a Visual Question Have Different Answers?](https://mlsanity.com/paper/1908.04342/) (2019)
- [Preregistering NLP Research](https://mlsanity.com/paper/2103.06944/) (2021)
- [SemEval-2021 Task 11: NLPContributionGraph -- Structuring Scholarly NLP Contributions for a Research Knowledge Graph](https://mlsanity.com/paper/2106.07385/) (2021)
- [StereoKG: Data-Driven Knowledge Graph Construction for Cultural Knowledge and Stereotypes](https://mlsanity.com/paper/2205.14036/) (2022)
- [Shallow Discourse Annotation for Chinese TED Talks](https://mlsanity.com/paper/2003.04032/) (2020)
- [Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains](https://mlsanity.com/paper/1906.02564/) (2019)
- [Correct Me If You Can: Learning from Error Corrections and Markings](https://mlsanity.com/paper/2004.11222/) (2020)
- [Query-Focused Opinion Summarization for User-Generated Content](https://mlsanity.com/paper/1606.05702/) (2016)
- [Exploiting Social Media Content for Self-Supervised Style Transfer](https://mlsanity.com/paper/2205.08814/) (2022)
- [SuperSim: a test set for word similarity and relatedness in Swedish](https://mlsanity.com/paper/2104.05228/) (2021)

## Built on by (most cited first, 23 of 23)

- [The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels](https://mlsanity.com/paper/2405.05860/) (2024)
- [Rating Roulette: Self-Inconsistency in LLM-As-A-Judge Frameworks](https://mlsanity.com/paper/2510.27106/) (2025)
- [Value Profiles for Encoding Human Variation](https://mlsanity.com/paper/2503.15484/) (2025)
- [Conformalized Credal Set Predictors](https://mlsanity.com/paper/2402.10723/) (2024)
- [What about em? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns](https://mlsanity.com/paper/2305.16051/) (2023)
- [Beyond Consensus: Perspectivist Modeling and Evaluation of Annotator Disagreement in NLP](https://mlsanity.com/paper/2601.09065/) (2026)
- [Counting on Consensus: Selecting the Right Inter-annotator Agreement Metric for NLP Annotation and Evaluation](https://mlsanity.com/paper/2603.06865/) (2026)
- [Voices in a Crowd: Searching for Clusters of Unique Perspectives](https://mlsanity.com/paper/2407.14259/) (2024)
- [Evaluating AI systems under uncertain ground truth: a case study in dermatology](https://mlsanity.com/paper/2307.02191/) (2023)
- [Architectural Sweet Spots for Modeling Human Label Variation by the Example of Argument Quality: It's Best to Relate Perspectives!](https://mlsanity.com/paper/2311.03153/) (2023)
- [A Taxonomy of Rater Disagreements: Surveying Challenges & Opportunities from the Perspective of Annotating Online Toxicity](https://mlsanity.com/paper/2311.04345/) (2023)
- [Perspectives in Play: A Multi-Perspective Approach for More Inclusive NLP Systems](https://mlsanity.com/paper/2506.20209/) (2025)
- [Optimal Conformal Prediction under Epistemic Uncertainty](https://mlsanity.com/paper/2505.19033/) (2025)
- [eMotions: A Large-Scale Dataset and Audio-Visual Fusion Network for Emotion Analysis in Short-form Videos](https://mlsanity.com/paper/2508.06902/) (2025)
- [Learning to Explore and Select for Coverage-Conditioned Retrieval-Augmented Generation](https://mlsanity.com/paper/2407.01158/) (2024)
- [P1SCO: Social Dimensions from a Perspectivist Lens](https://mlsanity.com/paper/2605.25312/) (2026)
- [Contrastive ESA: Human Evaluation of Multiple Translations at Once](https://mlsanity.com/paper/2607.26640/) (2026)
- [Malaysian English News Decoded: A Linguistic Resource for Named Entity and Relation Extraction](https://mlsanity.com/paper/2402.14521/) (2024)
- [Linguistic Fingerprint in Transformer Models: How Language Variation Influences Parameter Selection in Irony Detection](https://mlsanity.com/paper/2406.02338/) (2024)
- [From Ground Trust to Truth: Disparities in Offensive Language Judgments on Contemporary Korean Political Discourse](https://mlsanity.com/paper/2509.14712/) (2025)
- [Exploiting the English Grammar Profile for L2 grammatical analysis with LLMs](https://mlsanity.com/paper/2603.17171/) (2026)
- [Not All Subjectivity Is the Same! Defining Desiderata for the Evaluation of Subjectivity in NLP](https://mlsanity.com/paper/2603.28351/) (2026)
- [Boundaries Agree, Labels Do Not: Intra-Annotator Dynamics as a Kind of Training Data](https://mlsanity.com/paper/2610.04370/) (2026)

## Abstract (arXiv)

We commonly use agreement measures to assess the utility of judgements made by human annotators in Natural Language Processing (NLP) tasks. While inter-annotator agreement is frequently used as an indication of label reliability by measuring consistency between annotators, we argue for the additional use of intra-annotator agreement to measure label stability (and annotator consistency) over time. However, in a systematic review, we find that the latter is rarely reported in this field. Calculating these measures can act as important quality control and could provide insights into why annotators disagree. We conduct exploratory annotation experiments to investigate the relationships between these measures and perceptions of subjectivity and ambiguity in text items, finding that annotators provide inconsistent responses around 25% of the time across four different NLP tasks.