Pith. sign in

REVIEW 3 major objections 5 minor 1 cited by

From Disagreement to Understanding: The Case for Ambiguity Detection in NLI

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Annotation disagreement in NLI is not noise: the paper argues it often reflects ambiguity in the premise or hypothesis, and that inference systems should detect and classify that ambiguity before judging entailment.

desk verdict A genuinely useful position paper with a valuable unified taxonomy and framework, but the central 'process-independent' claim is in tension with its own proposed detection methods and remains empirically untested. read the letter →

arxiv 2507.15114 v2 pith:RDJDTUTL submitted 2025-07-20 cs.CL

Chathuri Jayaweera , Bonnie J. Dorr This is my paper · ORCID
classification cs.CL
keywords naturallanguageinferenceannotationdisagreementambiguitydetectionclassificationlabelvariationperspectivismtextualentailmenttaxonomy
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This position paper argues that annotator disagreement in Natural Language Inference is not mostly random noise but a meaningful signal, especially when it comes from ambiguity in the premise or hypothesis itself. It proposes reordering the NLI pipeline: first detect whether an input pair is ambiguous, then classify the type of ambiguity, then generate disambiguated readings, and only then perform inference. The paper synthesizes existing taxonomies into four broad ambiguity categories and argues that content-based ambiguity is the only disagreement source that can be addressed directly from the text, without needing annotation guidelines or annotator metadata. If the argument holds, NLI systems would move from picking a single majority label toward representing multiple coexisting human interpretations.

What carries the argument

The load-bearing machinery is the four-stage pipeline in Figure 1: ambiguity detection, ambiguity classification, disambiguation generation, and inference classification, with linguistic and background knowledge informing each stage. The second main component is the unified ambiguity taxonomy, which organizes subtypes such as Lexical, Scopal, Presupposition, Implicature, and Imperfections under four broad categories. This taxonomy carries the argument by giving detection and classification a common target language, making the proposed shift from label-distribution modeling to ambiguity-aware inference concrete enough to operationalize.

What would settle it

Take a sample of high-disagreement premise-hypothesis pairs, have linguistically trained annotators independently mark whether disagreement traces to ambiguity in the text and which subtype, then check whether content-based ambiguity accounts for most cases and whether automated detectors can recover those labels from the text alone; if most high-disagreement pairs are judged free of content-based ambiguity, or if the ambiguity types cannot be identified without extra context, the paper's central claim fails.

Watch

Extended reading notes

Core claim

The central claim is that content-based ambiguity in the premise or hypothesis is a root cause of many reproducible human label differences in NLI, distinct from unclear guidelines and annotator behavior. Because this ambiguity lives in the language itself, it is a process-independent signal: detecting it does not require knowing which annotator responded how or which instructions they saw. The paper concludes that ambiguity detection and classification should be explicit first stages of NLI modeling, followed by generating disambiguated versions and then inferring entailment for each interpretation. It also introduces a unified taxonomy grouping ambiguity into Lexical, Syntactic, Semantic, and Pragmatic types, and argues that current systems that only model annotator label distributions miss the reasons why interpretations diverge.

Load-bearing premise

The argument assumes ambiguity in the wording itself is common enough to explain a meaningful share of NLI disagreement and can be detected and classified from the premise-hypothesis text alone, without needing guideline information or annotator metadata.

Editorial extensions

If this is right

  • If ambiguity is detected before inference, majority-vote preprocessing becomes unnecessary for ambiguous pairs and can be replaced by multiple interpretable labels, each tied to a distinct reading.
  • A unified taxonomy of ambiguity types gives detection methods a shared vocabulary across datasets, making it possible to compare systems on whether they identify the same kinds of ambiguity.
  • Fact-verification pipelines that use NLI could flag claims whose wording is ambiguous or intentionally misleading instead of silently committing to one interpretation.
  • New datasets annotated for ambiguity presence and type become a necessary prerequisite, reshaping how NLI benchmarks are constructed and evaluated.
  • Explanations of model predictions could name a specific ambiguity type, giving a more direct account of why a premise-hypothesis pair admits multiple judgments.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper leaves open how to obtain training data for the detection stage; one testable extension is to use annotator agreement statistics as noisy supervision and check whether the resulting ambiguity labels match expert-annotated subtypes.
  • Because a single pair can exhibit several ambiguity types at once, the unified taxonomy likely needs to be treated as multi-label in evaluation, a case the paper does not discuss.
  • If the framework works, ambiguous pairs become a diagnostic tool: models that can hold multiple readings simultaneously would pass tests that single-label benchmarks cannot express.
  • The taxonomy's fuzzy boundaries between types such as Lexical and Syntactic ambiguity may make classification harder than the four broad categories suggest, so subtype-level inter-annotator agreement would be a natural early experiment.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This position paper argues that annotation disagreement in NLI often reflects genuine interpretive variation, specifically content-based ambiguity in the premise or hypothesis, rather than mere noise. The authors propose a two-stage framework in which NLI systems first detect whether an input pair is ambiguous, then classify the ambiguity type into a unified taxonomy (Lexical, Syntactic, Semantic, Pragmatic), generate disambiguated versions, and only then perform inference. The paper reviews existing work on annotator distribution modeling and disagreement sources, synthesizes three prior ambiguity taxonomies into Figure 7, and lays out a research agenda for creating ambiguity-annotated datasets and developing detection/classification methods.

Significance. If the central claim is correct, the paper identifies a concrete gap in current NLI research: existing systems model disagreement distributions but do not explain why interpretations diverge. The proposed framework and unified taxonomy are a useful synthesis that could support more explainable, human-aligned NLI systems. The paper is honest about its limitations, explicitly stating that empirical validation remains future work. Its main value is as a position piece that organizes existing evidence and provides a structured agenda for ambiguity-aware NLI. However, the strength of the load-bearing claims—that content-based ambiguity is prevalent, separable, and reliably detectable without external supervision—currently exceeds the evidence provided.

major comments (3)
  1. [Section 4 and Section 6] The claim that content-based ambiguity is a 'process-independent signal' that 'requires no external filtering or supervision' is contradicted by the paper's own proposed detection methodology. Section 6 recommends using cues from 'annotation patterns,' and Section 5.1 proposes leveraging datasets annotated with disambiguations and explanations, and using LLMs as evaluators. Section 3.1 also notes that the taxonomy is based on manually analyzed samples by linguistically trained annotators. If detecting ambiguity requires disagreement labels, ambiguity annotations, or manual analysis, then the distinguishing advantage of the two-stage framework over existing disagreement-detection methods is not established. The authors should either weaken the process-independence claim or specify precisely what information is available to the ambiguity detection stage in the intended setting.
  2. [Section 4] The assertion that 'Jiang and de Marneffe (2022)'s findings indicate that the most common sources of disagreement fall under content-based ambiguity' is not supported by any prevalence figures in this paper. No quantitative data are provided to show that ambiguity is a major driver of disagreement relative to guideline underspecification or annotator behavior. Since the paper's central motivational premise is that ambiguity is prevalent enough to warrant a dedicated framework, the authors should report the relevant statistics from the cited work (e.g., the proportion of disagreement instances attributed to each source) or explicitly qualify the claim as an interpretation of that work.
  3. [Section 5.1 and Limitations] The paper concedes that no datasets exist that are annotated for ambiguity and that current models perform below human-level accuracy on ambiguity detection (Section 5.1, Limitations). As a position paper, this is an acceptable limitation, but the abstract and Section 6 make causal claims that go beyond this: e.g., that ambiguity detection and classification 'enable' more robust, explainable, and human-aligned NLI. The central claim—that ambiguity is a separable, reliably detectable driver of disagreement—is asserted rather than demonstrated. I recommend framing the framework explicitly as a hypothesis to be tested, and providing a more concrete falsifiable prediction, such as an expected performance improvement on ambiguity-aware benchmarks once such annotations exist.
minor comments (5)
  1. [Section 5.1] Typo: 'disambigutations' should be 'disambiguations'.
  2. [Limitations] Typo: 'hybid' should be 'hybrid'.
  3. [Section 5.1] The dataset referred to as 'Ambient dataset' is more commonly known as 'AmbiEnt' (Liu et al., 2023); please standardize the name for reader searchability.
  4. [Figure 7] The mapping from the source taxonomies to the four broad categories is not explained. For example, 'Coreferential' is placed under Pragmatic, and 'Presupposition' under Semantic, but the criteria for these assignments are not stated. Even a brief description of the organizing principle would improve the taxonomy's usability and reproducibility.
  5. [Section 2] The paper mentions 'within-label variation' in Section 5 but does not define it until later; consider defining it at first use or adding a pointer.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is a position piece with no fitted parameters or derived quantities; its claims are asserted and cited from external work.

full rationale

This is a position paper arguing for ambiguity-aware NLI; it contains no equations, no fitted parameters, and no empirical predictions. Its contribution is a proposed framework and a unified taxonomy that explicitly synthesizes prior taxonomies (Jiang and de Marneffe 2022; Liu et al. 2023; Li et al. 2024) rather than presenting a derived result. The central claim that content-based ambiguity is a process-independent signal is asserted and supported by citations to external work, not established tautologically. The only self-citation (Jayaweera et al. 2024) appears in a passing fact-verification example and is not load-bearing. The Limitations section explicitly concedes that empirical validation is future work, so there is no fitted-input-called-prediction pattern. A tension between the claimed process-independent status and the proposed detection cues (annotation patterns, LLM evaluators) is a supportability concern, not a circular derivation: no claim is made true by its own definition. Accordingly, no circular step can be exhibited under the required standard of quoting a specific reduction.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No free parameters, fitted values, or invented entities. The paper's argument rests on domain assumptions about the nature of annotator disagreement and on its own taxonomy synthesis, both listed as axioms. There is no mathematical derivation.

assumptions (4)
  • domain assumption Annotator disagreement in NLI is often caused by legitimate interpretive variation, especially content-based ambiguity, rather than random noise.
    The paper inherits this from Pavlick and Kwiatkowski (2019) and Jiang and de Marneffe (2022); it is not independently established here.
  • ad hoc to paper Content-based ambiguity is the only disagreement source that can be systematically addressed through computational modeling without extra information about annotators or guidelines.
    Asserted in Section 4 as the basis for focusing on ambiguity; no empirical comparison demonstrates that guideline or behavior effects cannot be modeled similarly.
  • ad hoc to paper The ambiguity types from the three cited taxonomies can be faithfully organized into the four broad categories Lexical, Syntactic, Semantic, Pragmatic shown in Figure 7.
    This unification is the paper's own contribution; it is presented without inter-annotator agreement or formal mapping, so the synthesis itself is assumed.
  • domain assumption There are currently no systems that identify or classify ambiguity types in NLI.
    Stated as 'to our knowledge' in Sections 5 and 5.2; a negative existence claim that depends on completeness of the literature search.

how reviews work

0 comments
Cite this review

Pith. "Pith review of From Disagreement to Understanding: The Case for Ambiguity Detection in NLI." pith.science (2026). https://pith.science/paper/RDJDTUTL

@misc{pith2026250715114,
  author       = {Pith},
  title        = {Pith review of: From Disagreement to Understanding: The Case for Ambiguity Detection in NLI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RDJDTUTL}},
  note         = {Machine review of arXiv:2507.15114}
}
read the original abstract

This position paper argues that annotation disagreement in Natural Language Inference (NLI) is not mere noise but often reflects meaningful variation, especially when triggered by ambiguity in the premise or hypothesis. While underspecified guidelines and annotator behavior contribute to variation, content-based ambiguity provides a process-independent signal of divergent human perspectives. We call for a shift toward ambiguity-aware NLI that first identifies ambiguous input pairs, classifies their types, and only then proceeds to inference. To support this shift, we present a framework that incorporates ambiguity detection and classification prior to inference. We also introduce a unified taxonomy that synthesizes existing taxonomies, illustrates key subtypes with examples, and motivates targeted detection methods that better align models with human interpretation. Although current resources lack datasets explicitly annotated for ambiguity and subtypes, this gap presents an opportunity: by developing new annotated resources and exploring unsupervised approaches to ambiguity detection, we enable more robust, explainable, and human-aligned NLI systems.

Figures

Figures reproduced from arXiv: 2507.15114 by the authors.

Figure 1
Figure 1. Framework for ambiguity-aware NLI: First [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The taxonomy of disagreement sources de [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Probabilistic Enrichment ambiguity: Annota [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Temporal Reference disagreement: Annota [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: High Overlap disagreement: Annotators may [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]
Figure 6
Figure 6. Figure 6: Implicature Ambiguity: Annotators may infer [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 7
Figure 7. Figure 7: Unified ambiguity type taxonomy: We build [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. How Much Human Label Variation Does Formal Semantic Structure Explain?: Group-Level Effects and Item-Level Ceilings in NLI

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Formal semantic structure explains only 3.3–3.6% of label-entropy variance in ChaosNLI-S/M and does not detectably change what annotators disagree about.

Reference graph

Works this paper leans on

27 extracted references · 8 canonical work pages · cited by 1 Pith paper

  1. [1]

    Lora Aroyo and Chris Welty. 2015. https://doi.org/10.1609/aimag.v36i1.2564 Truth Is a Lie : Crowd Truth and the Seven Myths of Human Annotation . AI Magazine, 36(1):15--24. Number: 1

    doi:10.1609/aimag.v36i1.2564 2015

  2. [2]

    Samuel Bayer, John Burger, Lisa Ferro, John Henderson, and Alexander Yeh. 2005. MITRE ’s Submissions to the EU Pascal RTE Challenge . In Proceedings of the Pattern Analysis , Statistical Modelling , and Computational Learning ( PASCAL ) Challenges Workshop on Recognising Textual Entailment , pages 41--44

    work page 2005

  3. [3]

    Bowman, Gabor Angeli, Christopher Potts, and Christopher D

    Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. https://doi.org/10.18653/v1/D15-1075 A large annotated corpus for learning natural language inference . In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing , pages 632--642, Lisbon, Portugal. Association for Computational Linguistics

    doi:10.18653/v1/d15-1075 2015

  4. [4]

    Tongfei Chen, Zhengping Jiang, Adam Poliak, Keisuke Sakaguchi, and Benjamin Van Durme. 2020. https://doi.org/10.18653/v1/2020.acl-main.774 Uncertain Natural Language Inference . In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , pages 8772--8779, Online. Association for Computational Linguistics

    doi:10.18653/v1/2020.acl-main.774 2020

  5. [5]

    Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005. https://doi.org/10.1007/11736790_9 The PASCAL recognising textual entailment challenge . In Proceedings of the First international conference on Machine Learning Challenges : evaluating Predictive Uncertainty Visual Object Classification , and Recognizing Textual Entailment , MLCW '05, pages 177--190, ...

    doi:10.1007/11736790_9 2005

  6. [6]

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre -training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers) , pages 4171--4186

    work page 2019

  7. [7]

    Reto Gubelmann, Aikaterini-lida Kalouli, Christina Niklaus, and Siegfried Handschuh. 2023. https://doi.org/10.18653/v1/2023.starsem-1.4 When Truth Matters - Addressing Pragmatic Categories in Natural Language Inference ( NLI ) by Large Language Models ( LLMs ) . In Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (* SEM 2023...

    doi:10.18653/v1/2023.starsem-1.4 2023

  8. [8]

    Chathuri Jayaweera, Sangpil Youm, and Bonnie J Dorr. 2024. https://doi.org/10.18653/v1/2024.fever-1.26 AMREx : AMR for Explainable Fact Verification . In Proceedings of the Seventh Fact Extraction and VERification Workshop ( FEVER ) , pages 234--244, Miami, Florida, USA. Association for Computational Linguistics

    doi:10.18653/v1/2024.fever-1.26 2024

Show all 27 references
  1. [9]

    Nan-Jiang Jiang and Marie-Catherine de Marneffe. 2022. https://doi.org/10.1162/tacl_a_00523 Investigating Reasons for Disagreement in Natural Language Inference . Transactions of the Association for Computational Linguistics, 10:1357--1374. Place: Cambridge, MA Publisher: MIT Press

    2022 doi

  2. [10]

    Nan-Jiang Jiang, Chenhao Tan, and Marie-Catherine de Marneffe. 2023. https://doi.org/10.18653/v1/2023.findings-emnlp.712 Ecologically Valid Explanations for Label Variation in NLI . In Findings of the Association for Computational Linguistics : EMNLP 2023 , pages 10622--10633,...

    2023 doi

  3. [11]

    Li, Alisa Liu, Zhaofeng Wu, and Noah A

    Margaret Y. Li, Alisa Liu, Zhaofeng Wu, and Noah A. Smith. 2024. https://doi.org/10.48550/arXiv.2403.14072 A Taxonomy of Ambiguity Types for NLP . arXiv preprint. ArXiv:2403.14072 [cs]

    2024 doi arXiv

  4. [12]

    Alisa Liu, Zhaofeng Wu, Julian Michael, Alane Suhr, Peter West, Alexander Koller, Swabha Swayamdipta, Noah Smith, and Yejin Choi. 2023. https://doi.org/10.18653/v1/2023.emnlp-main.51 We're Afraid Language Models Aren 't Modeling Ambiguity . In Proceedings of the 2023 Conferenc...

    2023 doi

  5. [13]

    Christopher D. Manning. 2006. https://api.semanticscholar.org/CorpusID:60340094 LOCAL TEXTUAL INFERENCE : IT ' S HARD TO CIRCUMSCRIBE , BUT YOU KNOW IT WHEN YOU SEE IT - AND NLP NEEDS IT

    2006

  6. [14]

    Johannes Mario Meissner, Napat Thumwanit, Saku Sugawara, and Akiko Aizawa. 2021. https://doi.org/10.18653/v1/2021.acl-short.109 Embracing Ambiguity : Shifting the Training Target of NLI Models . In Proceedings of the 59th Annual Meeting of the Association for Computational Lin...

    2021 doi

  7. [15]

    Yixin Nie, Xiang Zhou, and Mohit Bansal. 2020. https://doi.org/10.18653/v1/2020.emnlp-main.734 What Can We Learn from Collective Human Opinions on Natural Language Inference Data ? In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing ( EMNL...

    2020 doi

  8. [16]

    Boyuan Pan, Yazheng Yang, Zhou Zhao, Yueting Zhuang, Deng Cai, and Xiaofei He. 2018. https://doi.org/10.18653/v1/P18-1091 Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference . In Proceedings of the 56th Annual Meeting of the Associatio...

    2018 doi

  9. [17]

    Hancheol Park and Geonmin Kim. 2025. https://aclanthology.org/2025.coling-industry.38/ Where do LLMs Encode the Knowledge to Assess the Ambiguity ? In Proceedings of the 31st International Conference on Computational Linguistics : Industry Track , pages 445--452, Abu Dhabi, UA...

    2025

  10. [18]

    Ellie Pavlick and Tom Kwiatkowski. 2019. https://doi.org/10.1162/tacl_a_00293 Inherent Disagreements in Human Textual Inferences . Transactions of the Association for Computational Linguistics, 7:677--694. Place: Cambridge, MA Publisher: MIT Press

    2019 doi

  11. [19]

    Problem

    Barbara Plank. 2022. https://doi.org/10.18653/v1/2022.emnlp-main.731 The “ Problem ” of Human Label Variation : On Ground Truth in Data , Modeling and Evaluation . In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing , pages 10671--10682, A...

    2022 doi

  12. [20]

    Rion Snow, Brendan O'Connor, Daniel Jurafsky, and Andrew Ng. 2008. https://aclanthology.org/D08-1027/ Cheap and Fast – But is it Good ? Evaluating Non - Expert Annotations for Natural Language Tasks . In Proceedings of the 2008 Conference on Empirical Methods in Natural Langua...

    2008

  13. [21]

    James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018. https://doi.org/10.48550/arXiv.1803.05355 FEVER : a large-scale dataset for Fact Extraction and VERification . arXiv preprint. ArXiv:1803.05355

    2018 doi arXiv

  14. [22]

    Leon Weber-Genzel, Siyao Peng, Marie-Catherine De Marneffe, and Barbara Plank. 2024. https://doi.org/10.18653/v1/2024.acl-long.123 VariErr NLI : Separating Annotation Error from Human Label Variation . In Proceedings of the 62nd Annual Meeting of the Association for Computatio...

    2024 doi

  15. [23]

    Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. https://doi.org/10.18653/v1/N18-1101 A Broad - Coverage Challenge Corpus for Sentence Understanding through Inference . In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computatio...

    2018 doi

  16. [24]

    Xinliang Frederick Zhang and Marie-Catherine de Marneffe. 2021. https://doi.org/10.18653/v1/2021.naacl-main.390 Identifying inherent disagreement in natural language inference . In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computat...

    2021 doi

  17. [25]

    Xiang Zhou, Yixin Nie, and Mohit Bansal. 2022. https://doi.org/10.18653/v1/2022.findings-acl.79 Distributed NLI : Learning to Predict Human Opinion Distributions for Language Reasoning . In Findings of the Association for Computational Linguistics : ACL 2022 , pages 972--987, ...

    2022 doi

  18. [26]

    online" 'onlinestring :=

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list...

  19. [27]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.