Modelling Adjectival Modification Effects on Semantic Plausibility

Anna Golub; Annerose Eichel; Beate Zywietz

REVIEW 4 major objections 6 minor 22 references

Modelling Adjectival Modification Effects on Semantic Plausibility

T0 review · 4 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper claims that sentence transformers underperform transformer classifiers on the ADEPT plausibility-change task, and that class imbalance distorts reported performance.

desk verdict Useful negative result and a sensible evaluation critique, but the headline ST-underperformance claim rests on a small gap and a duplicate-reuse bug in the proposed cross-balanced protocol. read the letter →

arxiv 2507.21828 v1 pith:AA7A7BLM submitted 2025-07-29 cs.CL

Anna Golub , Beate Zywietz , Annerose Eichel This is my paper · ORCID

classification cs.CL

keywords semanticplausibilityadjectivalmodificationeventsentencetransformerstransformerclassifiersclassimbalancecross-balancedevaluationADEPTbenchmark

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

The paper asks whether language models can detect how adding one adjective changes whether an event is plausible, for example turning "a horse goes away" into "a dead horse goes away." It introduces sentence transformers as a new approach to the ADEPT benchmark and compares them with BERT, RoBERTa, DeBERTa, and MPNet classifiers. The central claim is that sentence transformers, despite being conceptually suited to comparing sentence pairs, do not beat standard transformer classifiers under a balanced evaluation, and that imbalanced training and test data distort earlier-looking conclusions. The authors propose cross-balanced evaluation, which averages scores across windows over all classes, as a more trustworthy estimate of model ability.

What carries the argument

The central object is the ADEPT benchmark: 16,115 English sentence pairs differing by exactly one adjective, labeled for whether the modified event is less, equally, or more plausible. The paper adapts it to a three-class version and introduces cross-balanced evaluation, where a window of size equal to the smallest class is moved across each class's test instances and scores are averaged, so every instance contributes equally. Sentence transformers are the new modeling device: an MPNet backbone maps each pair to a cosine similarity, with labels converted to 0, 0.5, and 1 and thresholds dividing the cosine range into three bins. This machinery lets the paper compare architectures under controlled class balance.

What would settle it

Run the same fine-tuning and evaluation on the original five-class ADEPT with the 'impossible' and 'necessarily true' labels cleaned of the noisy annotations the paper cites; if a sentence transformer then matches or beats RoBERTa and MPNet under cross-balanced evaluation, the paper's underperformance claim falls.

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Extended reading notes

Core claim

On the adapted three-class ADEPT task (less likely, equally likely, more likely), the paper's cross-balanced evaluation shows the best sentence transformer reaches F1-macro 0.59 when trained on balanced data, while RoBERTa and MPNet classifiers reach 0.61; under standard evaluation the gap widens, with the sentence transformer dropping to 0.410 versus RoBERTa's 0.471. When trained on the imbalanced full data, the roles reverse: the sentence transformer reaches 0.562 F1-macro in cross-balanced evaluation, outperforming the transformer classifiers. The paper reads both results as evidence that no model is close to robust performance, and that the training and test balance changes which architecture looks best.

Load-bearing premise

The load-bearing premise is that the adapted three-class, rebalanced version of ADEPT still measures the same plausibility-change ability as the original five-class benchmark, so conclusions about which models struggle transfer to the original task.

Editorial extensions

If this is right

If the central claim is right, published accuracy numbers on the original imbalanced ADEPT test set overstate model ability, because a majority-class predictor can appear strong.
Balanced training data plus cross-balanced evaluation becomes the default way to compare plausibility-change models, and rankings under standard evaluation should be treated as provisional.
Sentence transformers should not be assumed to be the natural fit for pairwise plausibility change just because they compare sentence pairs; their scalar cosine-similarity output needs stronger calibration or a different training signal.
Future model development should target the errors revealed by balanced confusion matrices, where 'equally likely' instances are split into 'less likely' and 'more likely' rather than collapsed into the majority class.

Reading between the lines

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

A natural extension would be to apply the cross-balanced evaluation method to other imbalanced multi-class NLP benchmarks, where it could expose how much of a reported F1 score is an artifact of class priors.
The sentence transformers' weakness may stem from their training objective rather than their architecture; contrastive fine-tuning on plausibility-change pairs, instead of similarity-score regression, could change the ranking.
The paper leaves open whether the excluded extreme classes ('impossible' and 'necessarily true') carry signal; if those classes are cleaned rather than dropped, a five-class version could show a different model ranking.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: this is a transparent, well-written empirical paper that does three useful things. It replicates Emami et al.'s ADEPT baselines, adds sentence transformers as a conceptually different model family, and proposes a balanced evaluation protocol to counter the dataset's heavy skew. The main pattern—models struggle, and sentence transformers are not clearly better—is plausible. But the paper's headline claim that ST underperform is overstated, and the new evaluation protocol has a statistical flaw that undermines the significance test behind the main comparison.

The novelty is real but modest. Applying sentence transformers to ADEPT is new, and the cross-balanced window idea is a fresh way to report class-balanced performance without throwing away the original splits. The paper is also honest: it acknowledges the thresholds and label mappings for ST were tuned, it releases code, and it compares carefully with prior work's evaluation choices.

Now the soft spots, in proportion. The biggest one is the cross-balanced protocol. When a class size is not a multiple of the window size, wrapping reuses instances near the boundary, so those instances contribute twice to the averaged predictions. The ASO test then runs on a sample with duplicated data, violating independence and inflating the effective sample size. With only three seeds and no error bars, the 0.024 F1-macro gap between ST-balanced and RoBERTa/MPNet-balanced in Table 2 may well be noise. This is fixable—don't wrap, or use a different balancing scheme—but until it's fixed, the significance claim should not be taken at face value.

Relatedly, the abstract's blanket statement that sentence transformers underperform is internally contradicted by Section 4, where ST-full actually achieves the highest cross-balanced F1-macro. The underperformance holds only under balanced training, and even there the gap is small and statistically fragile as argued above.

The benchmark modification—removing 'impossible' and 'necessarily true' and down-sampling 'equally likely'—is a limitation but not fatal. The authors justify it with low annotator agreement, and it affects both model families equally. Still, conclusions about model struggle are tied to this adapted 3-class setting, not the original 5-class ADEPT.

Who is this for? Researchers working on plausibility or sentence-pair evaluation benchmarks. The imbalance critique, independent of the protocol bug, is a valid reminder that accuracy on skewed data is meaningless and that class-balanced reporting matters.

Recommendation: this deserves peer review, but as a major-revision case. The evaluation protocol needs to be fixed or re-analyzed without wrap-around duplication, the significance claim needs to be re-run, and the conclusion should be carefully qualified. The paper's core message—that imbalance distorts evaluation and that sentence transformers don't automatically win on a similarity-flavored task—can survive those changes.

Referee Report

4 major / 6 minor

Summary. The paper addresses the task of predicting changes in semantic plausibility triggered by adjectival modification, using the ADEPT benchmark. The authors adapt ADEPT by removing the 'impossible' and 'necessarily true' classes and down-sampling 'equally likely', then fine-tune transformer classifiers (BERT, RoBERTa, DeBERTa, MPNet) and a sentence transformer (ST, MPNet-based) on both balanced and imbalanced versions of the training data. They propose a 'cross-balanced' evaluation protocol that slides a window over each class, wrapping around at the end, and compare results to standard evaluation. The main claims are: (i) both model families struggle with the task; (ii) sentence transformers underperform transformer-based classifiers despite conceptual alignment; and (iii) class imbalance in training and test data distorts reported performance. The paper also compares its best models to previous results from Emami et al. and presents a confusion-matrix analysis for one seed.

Significance. If the central claims held, the paper would provide a useful benchmark modification and evaluation protocol for studying semantic plausibility changes, and a noteworthy negative result about sentence transformers on this task. The experiments are reproducible with public code, and the paper does show a consistent pattern that balanced training and cross-balanced evaluation shift model rankings relative to standard practice. The 'models struggle' finding is credible and consistent with prior work on ADEPT. However, the headline claim that sentence transformers underperform is contradicted by the paper's own cross-balanced results for full-data training (ST-full is best), and the statistical significance of the balanced-condition gap is undermined by a design flaw in the cross-balanced protocol. These issues affect the main conclusions and require revision.

major comments (4)

[Abstract; Section 4, Table 2] The blanket claim in the abstract and conclusion that sentence transformers underperform is not supported by the paper's own results. In Table 2, under cross-balanced evaluation, ST-full achieves the highest F1-macro (0.562) among models trained on full data, exceeding DeBERTa-full (0.529) and RoBERTa-full (0.521). The underperformance holds only in the balanced-training condition, where ST-balanced (0.588) trails RoBERTa/MPNet-balanced (0.612) by 0.024. The abstract and conclusion must be qualified to specify that the underperformance is observed for the balanced-training setup, not as a general property.
[Section 3.4; Section 4] The cross-balanced evaluation protocol with wraparound reuses test instances when a class size is not a multiple of the window size s. For a class of size n with n mod s != 0, the wrapping causes some instances to appear in two iterations. These duplicated predictions are then averaged into the final balanced score and used in the ASO significance test, violating the i.i.d. assumption that ASO requires and inflating the effective sample size. With only three seeds and no per-seed error bars, the reported statistical significance of the 0.024 F1-macro gap between RoBERTa/MPNet-balanced and ST-balanced is not credible. The authors should report per-seed results, use non-overlapping evaluation folds, correct the significance test for duplicates, or provide bootstrap confidence intervals.
[Section 1; Section 4; Section 5] The paper repeatedly labels cross-balanced evaluation as 'more realistic' without providing a task-based justification. The original ADEPT distribution is heavily skewed by design, and forcing a balanced test distribution changes the evaluation task. The comparison to Emami et al. in Table 3 is confounded by class removals and different evaluation protocols, so it does not demonstrate that imbalance distorts performance; it only shows that different test distributions yield different scores. This claim should either be substantiated with an argument about how balanced evaluation better reflects real-world usage, or it should be tempered to 'alternative evaluation' rather than 'more realistic'.
[Section 3.3] The sentence transformer comparison is not parameter-free: the cosine label mapping (0, 0.5, 1) and the decision thresholds (0.33/0.66) are empirically tuned, as the text states. While the paper notes that different thresholds yield similar results, the comparison against classifiers with a learned output layer is not on equal footing. Please add a sensitivity analysis to the main text (e.g., a table showing ST performance across a range of thresholds), and discuss whether the 'conceptual alignment' claim holds if the mapping or thresholds change materially.

minor comments (6)

[Throughout] The dataset name is written as both 'Adept' and 'ADEPT'; please use a single consistent form.
[Section 3.2] The hyperparameter description is incomplete: please specify batch size, number of training steps or epochs, weight decay, and warmup if used, for all models so that the experiments can be reproduced exactly.
[Section 3.4] Please specify the actual window size s used in the cross-balanced evaluation, and explain how the wrapping is applied across classes of different sizes. This information is necessary for readers to evaluate the duplicate-instance issue.
[Table 3] The table compares RoBERTa-full standard accuracy to DeBERTa results with different class setups, and the discussion itself notes that the comparison is not direct. Consider adding a footnote that explicitly lists the class sets and evaluation protocols so the table is self-explanatory.
[Figure 2] The caption text appears to contain rendering artifacts in the provided manuscript (the second caption is garbled). Please regenerate the figure and verify that the captions are readable.
[Section 4] The statement 'performancescoreslagclearlybehindcross-balancedevaluation' has missing spaces; similar spacing inconsistencies appear in several places. A careful proofread is recommended.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's claims are empirical comparisons on a fixed benchmark, not derivations from their own outputs.

full rationale

The central claims — that transformer-based classifiers and sentence transformers both struggle on the adapted ADEPT task and that sentence transformers underperform RoBERTa/MPNet — are empirical measurements from fine-tuning and evaluating models on a fixed dataset. No equation in the paper defines the outcome in terms of the input, and no reported number is a fitted parameter renamed as a prediction. The sentence-transformer thresholds are chosen to split the cosine range mapped from the three target labels into equal thirds; while described as empirically determined, the paper states that different threshold sets do not improve performance and the equal-thirds split follows from the 0/0.5/1 label mapping, so this is a modeling choice rather than a circular reduction. The 'more realistic balanced evaluation' claim is an interpretive framing of the cross-balanced protocol, not a derivation from that protocol's definition. The paper cites prior work by one of its co-authors ([4],[5]) and by the ADEPT authors ([6]), but these citations are contextual related-work references and are not load-bearing for the main result. The cross-balanced evaluation's window wrap-around may reuse test items across iterations, which is a statistical-significance concern for the ASO comparison, but it is a correctness/validity issue, not circularity. Under the hard rule that circularity must be exhibited as a specific reduction to inputs, no such reduction exists here.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central comparison rests on a rebalanced, reduced version of ADEPT and on an ad hoc mapping of ordinal labels to cosine-similarity scores; both are design choices specific to this paper. No new theoretical entities are introduced.

free parameters (3)

Cosine label mapping for sentence transformers = 0 -> 0.0, 1 -> 0.5, 2 -> 1.0
Chosen in Section 3.3; the authors experimented with alternatives (e.g., -1/0/1) and selected this mapping, so it is a tuned design choice rather than derived.
Decision thresholds for sentence transformers = <=0.33, 0.34-0.65, >=0.66
Determined empirically after experimentation with other threshold sets (Section 3.3 footnote 2).
Down-sampling target for equally likely class = 1,500 sentence pairs
Chosen in Section 3.1 to match the size of 'less likely' and approach 'more likely'; a hand-picked size.

assumptions (4)

domain assumption The adapted 3-class ADEPT (excluding 'impossible' and 'necessarily true', down-sampling 'equally likely') faithfully represents the plausibility-change task.
Section 3.1 justifies exclusion via Emami et al.'s annotation-quality findings, but this changes the benchmark and the comparison to prior results.
ad hoc to paper Ordinal plausibility-change labels can be mapped to equally spaced cosine-similarity targets for Siamese training without loss of task structure.
Section 3.3 maps labels to 0/0.5/1 and thresholds to thirds of the cosine range; no theoretical argument is given that this preserves ordinal or class information.
ad hoc to paper Cross-balanced window evaluation with wraparound yields unbiased per-class performance estimates.
Section 3.4: windows that wrap around may reuse instances when class sizes are not divisible by window size s, and no correction is described.
domain assumption Human plausibility-change judgments in ADEPT are valid ground truth for the task.
All models are trained and evaluated against these labels (Section 3.1); the paper does not re-audit annotation quality.

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Cite this review

Pith. "Pith review of Modelling Adjectival Modification Effects on Semantic Plausibility." pith.science (2026). https://pith.science/paper/AA7A7BLM

@misc{pith2026250721828,
  author       = {Pith},
  title        = {Pith review of: Modelling Adjectival Modification Effects on Semantic Plausibility},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AA7A7BLM}},
  note         = {Machine review of arXiv:2507.21828}
}

read the original abstract

While the task of assessing the plausibility of events such as ''news is relevant'' has been addressed by a growing body of work, less attention has been paid to capturing changes in plausibility as triggered by event modification. Understanding changes in plausibility is relevant for tasks such as dialogue generation, commonsense reasoning, and hallucination detection as it allows to correctly model, for example, ''gentle sarcasm'' as a sign of closeness rather than unkindness among friends [9]. In this work, we tackle the ADEPT challenge benchmark [6] consisting of 16K English sentence pairs differing by exactly one adjectival modifier. Our modeling experiments provide a conceptually novel method by using sentence transformers, and reveal that both they and transformer-based models struggle with the task at hand, and sentence transformers - despite their conceptual alignment with the task - even under-perform in comparison to models like RoBERTa. Furthermore, an in-depth comparison with prior work highlights the importance of a more realistic, balanced evaluation method: imbalances distort model performance and evaluation metrics, and weaken result trustworthiness.

Figures

Figures reproduced from arXiv: 2507.21828 by the authors.

**Figure 1.** Three iterations of cross-balanced evaluation. The window (red) is shifted across dataset classes until every instance has been seen (not to scale). Standard evaluation For the sake of completeness and full comparability with previous work, we also evaluate our models the traditional way, i.e. on a singlerun through the dev or test set. Statistical significance testing To ensure the statistical significance of the … view at source ↗

**Figure 2.** Class-wise performance visualization. 6 Conclusion In this work, we tackled the task of predicting changes in semantic plausibility induced by a single adjectival modifier. Leveraging the Adept challenge dataset, we conducted an in-depth dataset suitability analysis. Our modeling experiments reveal that both transformer models and sentence transformers struggle with the task at hand, particularly under a more real… view at source ↗

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Pith tools

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

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In: 2023 IEEE International Conference on Indus- try 4.0, Artificial Intelligence, and Communications Technology (IAICT) (2023)

Alfianto, M.A., Priyadi, Y., Laksitowening, K.A.: Semantic Textual Similar- ity in Requirement Specification and Use Case Description based on Sen- tence Transformer Model. In: 2023 IEEE International Conference on Indus- try 4.0, Artificial Intelligence, and Communications Technology (IAICT) (2023). https://doi.org/10.1109/IAICT59002.2023.10205769

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In: Vla- chos, A., Augenstein, I

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