Boundary-Driven Table-Filling with Cross-Granularity Contrastive Learning for Aspect Sentiment Triplet Extraction

Jinghui Qin; Qingling Li; Wushao Wen

REVIEW 3 major objections 5 minor 24 references

Boundary-Driven Table-Filling with Cross-Granularity Contrastive Learning for Aspect Sentiment Triplet Extraction

T0 review · 3 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

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

Pith's one-line read BTF-CCL sets new F1 records on all four ASTE benchmarks

desk verdict Incremental but coherent ASTE extension; the reported gains are plausible yet rest on cross-paper baselines and single runs, so the SOTA claim needs reproducibility evidence before being taken at face value. read the letter →

arxiv 2502.01942 v1 pith:56P4BBXT submitted 2025-02-04 cs.CL cs.AI

Qingling Li , Wushao Wen , Jinghui Qin This is my paper · ORCID

classification cs.CLcs.AI

keywords aspectsentimenttripletextractiontablefillingcross-granularitycontrastivelearningboundary-drivenmulti-scaleconvolutionfine-grainedanalysisaspect-based

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 claims that aspect sentiment triplet extraction—locating the aspect words, the opinion words, and the sentiment that links them—is best done by filling a two-dimensional table of word-pair relations, and that existing table-filling methods lose global sentence context. To fix this, the authors propose BTF-CCL, which adds a cross-granularity contrastive loss that pulls a sentence-level representation toward the pooled word-pair table and pushes it away from tables of other sentences in the batch. A multi-scale, multi-granularity convolutional block is also inserted into the table pipeline to capture local and long-range patterns. On the four standard restaurant and laptop benchmarks, the paper reports F1 scores of 75.88, 63.29, 67.68, and 73.80, the best in the comparison table by margins of 0.7 to 1.56 points. If the comparison is fair, the result shows that sentence-level supervision is a low-cost way to strengthen word-level table-filling models.

What carries the argument

The central object is a two-dimensional relation table in which each cell represents the interaction between two words, and each aspect-opinion-sentiment triplet is a rectangular region delimited by boundary tags 'S' for the upper-left corner and 'E' for the lower-right corner. The argument is carried by two additions to the boundary-driven framework: a cross-granularity contrastive loss that aligns the sentence-level representation with the mean-pooled table representation, and an MMCNN block with multiple kernel sizes and dilation rates that refines the table before region detection and sentiment classification. Together they force the model to keep global sentence context aligned with local word-pair details.

What would settle it

Re-run BTF-CCL and all baselines from Table II in a single codebase with identical preprocessing, batch size, seeds, and ten-epoch validation-based early stopping. If re-implemented baselines equal or surpass the reported BTF-CCL F1 scores on any dataset, the claimed state-of-the-art result would not be reproduced as a real improvement.

Watch

Extended reading notes

Core claim

The central claim is that table-filling methods for ASTE attend only to word-level interactions and therefore mis-handle multi-word aspects and opinions in complex sentences. BTF-CCL takes the boundary-driven table-filling framework and adds two components: cross-granularity contrastive learning, in which the sentence-level [CLS] representation serves as an anchor, the mean-pooled table output serves as the positive sample, and pooled tables from other batch sentences serve as negatives, optimized by a margin-based ranking loss; and a multi-scale multi-granularity convolution block that applies 1x1, 3x3, and 5x5 kernels with dilations 1, 2, and 3 to the relation table to enrich local and long-range features. The paper reports consistent F1 gains over the strongest baselines on all four datasets, with the largest improvements on 16Res and 15Res (1.53 and 1.56 points), and its ablation study shows that removing either component lowers F1 on every dataset.

Load-bearing premise

The assumption that the previously published F1 numbers of the baselines can be compared directly to the authors' own evaluation pipeline, since the baselines were not re-run under the same preprocessing, seeds, and decoding settings.

Editorial extensions

If this is right

If the reported numbers hold, the method outperforms all compared baselines on 14Res, 14Lap, 15Res, and 16Res, improving F1 by 0.7 to 1.56 points over the previous best.
The ablation study implies that both the contrastive loss and the multi-scale convolution contribute; removing the contrastive loss drops F1 to as low as 62.01 on 14Lap, and removing the convolution block also lowers every score.
The approach requires no additional annotated data, since negative samples are drawn from other sentences in the same batch.
The reported gains come with a precision-recall trade-off: on 14Res and 14Lap the method has higher precision but lower recall than the span-based contrastive baseline.

Reading between the lines

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

The comparison relies on published F1 numbers for baselines rather than re-running them under identical conditions; a same-code re-run could shrink the reported 0.7 to 1.6 point margins.
The cross-granularity loss uses one pooled positive per sentence; alternative pooling that weights boundary cells or region interiors might align global context more tightly and is a natural next test.
The datasets are English restaurant and laptop reviews; whether sentence-level alignment helps in other domains or languages is left open.
The same contrastive loss could be applied to span-based or generative ASTE models, which would test whether the mechanism generalizes beyond table filling.

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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

Quick take: the paper does one thing clearly—it adds a cross-granularity contrastive loss between the sentence-level CLS vector and the pooled table representation of BDTF, plus a multi-scale CNN, and reports consistent F1 gains of 0.7–1.6 points on four public ASTE benchmarks. If those gains hold under reimplementation, it's a modest but real contribution. The architecture is coherent and the ablation is internally consistent: removing either component drops F1 on all four datasets.

What's new: the specific combination is new, though each ingredient is borrowed from cited work—BDTF for table filling, [17]/[20] for contrastive learning, standard CNN for multi-scale encoding. The cross-granularity idea, aligning global sentence representation with a pooled word-pair table, is the most interesting part and is worth testing in isolation.

The soft spots are the empirical ones. There is no code, no error bars, and no significance tests. The table caption admits the first four baselines are copied from [19] and the rest from the original papers, so no baseline is re-run in this pipeline. The margins are small, and the 14Lap margin is over the authors' own prior Span ASTE, whose number is externally reported. A single run selecting the best of 10 epochs by validation F1 can easily shift by more than 0.7 points. Also, the main text claims the model "outperforms the baseline models in terms of Precision, Recall, and F1"—Table II shows recall is lower than Span ASTE on 14Res and 14Lap, so that sentence is overbroad. The negative sampling in the contrastive loss is also underspecified.

None of this is fatal. The method is sensible and the reported improvements are consistent. But the central SOTA claim is not yet robustly supported. What this needs is code, a re-run of the main baselines in the same framework, and multi-seed evaluation with variance reporting. That is a standard ask for this subfield.

Verdict: send to review, but expect the authors to supply reproducibility evidence. I'd bring it to reading group as an example of how small gains and borrowed baselines can overclaim, but I wouldn't cite it in my own work until the numbers are reproducible.

Referee Report

3 major / 5 minor

Summary. The paper proposes BTF-CCL, a boundary-driven table-filling model for aspect sentiment triplet extraction (ASTE). The main contributions are a cross-granularity contrastive learning (CCL) module that aligns a sentence-level representation (CLS) with word/table-level representations, and a multi-scale multi-granularity convolution (MMCNN) module applied to the 2D relation table. The model is evaluated on the four standard ASTE benchmarks (14Res, 14Lap, 15Res, 16Res) and reported to achieve state-of-the-art F1 scores (75.88, 63.29, 67.68, 73.80 respectively). The paper also includes an ablation study removing CCL and MMCNN. The central claim is that the combined architecture yields consistent improvements over existing methods, with gains of 0.70–1.56 F1 points over the best prior baselines.

Significance. If the reported results are robust, the paper makes a modest but useful contribution to the ASTE literature by showing that explicitly aligning sentence-level and word-level representations can improve table-filling models, and that multi-scale convolutions over the 2D relation table help capture local and long-range dependencies. The architecture is coherent and the proposed components are clearly motivated. The use of public benchmarks and comparisons to established baselines is appropriate. However, the significance is limited by the empirical validation: the state-of-the-art claim rests on cross-paper comparisons without re-run baselines, single-seed runs, and no variance or significance testing. The gains are small enough that run-to-run variability or differences in evaluation pipelines could change the ranking. The paper would be significantly strengthened by a rigorous reproducible evaluation protocol, including multiple seeds and ideally re-implemented baselines.

major comments (3)

[Table II and §III-C] The central state-of-the-art claim in the abstract is supported only by cross-paper comparisons: the caption of Table II states that the first four baseline rows are copied from [19] and the remaining data are sourced from the respective papers, and no baseline is re-run under the authors' own pipeline. Because the reported margins over the best baseline are only 0.70–1.56 F1 points, differences in BERT checkpoint, preprocessing, decoding, random seeds, or validation selection across papers could plausibly account for the gains. The authors should either re-run all baselines in their own framework under identical conditions or provide a convincing argument, with additional evidence, that the published numbers are directly comparable. Without this, the abstract's "state-of-the-art" claim is not robustly established.
[§III-B and Table III] The paper reports a single run, trains for 10 epochs, and selects parameters based on the highest validation F1 before reporting test scores. No standard deviations, confidence intervals, or significance tests are provided. In the ablation study (Table III), the text in §III-E says the declines are "significant," but without multiple seeds or statistical testing this is an unsupported claim. Given the small F1 differences involved (e.g., 0.45–1.28 points in the ablations), the authors should report mean and standard deviation over at least five independent runs and, if possible, paired significance tests for both the main results and the ablations.
[§II-B.3 and Eq. (8)] The negative sampling procedure for the cross-granularity contrastive loss is underspecified. The text says "we adopt the local representations hneg from different sentences in the same batch," but it is not clear whether hneg is a single randomly chosen negative per anchor, an expectation over all negatives, or something else. Equation (8) shows only one negative in the margin-based ranking loss, but in a typical batch there would be many possible negatives. The number and selection strategy of negatives directly affect the learned representation, so this ambiguity is a reproducibility concern. Please specify exactly how negatives are sampled, whether the loss is summed or averaged over negatives, and how the margin m interacts with the batch composition.

minor comments (5)

[§II-B.1 heading] The subsection heading "Representation Leanrning" contains a typo; it should be "Representation Learning."
[Table II caption] The caption "The first four results are from [19], and the remaining data are sourced from the respective papers" is ambiguous because the table contains seven baseline rows plus the proposed method. Clarify which rows refer to which source, and identify whether "remaining data" includes the BTF-CCL row.
[§II-B.1 and Eq. (1)] The variables cij and tij in Eq. (1) are introduced but not defined. Please define the context representation and the tensor-based interaction operation, or give a citation where these are specified, so that the model is fully reproducible.
[§II-B.3 and Eq. (8)] The choice of Euclidean distance in a margin-based ranking loss is unusual for cross-granularity alignment; a brief justification or comparison to other contrastive objectives (e.g., InfoNCE) would help readers understand why this formulation is preferred.
[General] The paper does not mention code availability or a reproducibility statement. Including a link to released code or, at minimum, a detailed description of hyperparameters and implementation would substantially improve reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is an empirical architecture/evaluation study whose claims rest on public benchmark numbers, not on a derivation that reduces to its inputs.

full rationale

The central claim is an empirical one: BTF-CCL achieves the highest F1 among compared methods on four public ASTE benchmarks. There is no claimed derivation from first principles that could be equivalent to its inputs. The loss function (Eq. 15) combines contrastive, boundary, and classification terms, and the reported gains are measured against externally published baseline results in Table II; none of the reported scores is a fitted parameter renamed as a prediction. The only self-citation is to Span ASTE (POS&CL) [17], the authors' prior work, and it appears solely as one of the baseline rows in Table II and in the related-work paragraph. That citation is not load-bearing for the architecture or for the equation-level content; the boundary-driven table-filling base itself is attributed to the external work [19], and the contrastive and convolutional components are described within the paper with their own equations and ablations. The ablation study (Table III) compares the full model against variants in the same pipeline, so its internal comparisons are self-contained. The concern that cross-paper F1 numbers may not be perfectly comparable, that no variance or significance test is reported, and that a single run underlies the SOTA claim is a legitimate experimental-robustness or correctness-risk issue, but it is not a circularity issue under the criteria used here: no equation or definition from the paper reduces to an earlier equation or to a fitted value.

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

The work introduces no new theoretical entities. Its central empirical claim rests on a handful of hand-chosen hyperparameters and standard modeling assumptions; the most fragile assumption is cross-paper comparability of baseline scores.

free parameters (4)

Contrastive loss margin m = 1.0
Set by hand in Eq. (8); controls the separation between positive and negative sample pairs.
MMCNN kernel sizes and dilation rates = 3x3 and 5x5 kernels; dilations 1, 2, 3
Chosen by hand in Section II-B2 to capture multi-scale local information; no search or sensitivity analysis is reported.
Contrastive loss weight = 1.0 (implicit)
LCL is added directly to the total loss in Eq. (15) with no tuning or reported weight search.
Number of training epochs = 10
Section III-B trains for 10 epochs and selects the best model by validation F1; no early stopping or epoch sensitivity analysis is reported.

assumptions (4)

domain assumption The BERT final-layer hidden states contain sufficient context to initialize word-pair relation representations in Eq. (1).
Section II-B1 builds the entire table from these embeddings; if they lack task-relevant signals, the downstream modules cannot recover them.
domain assumption Each aspect-opinion-sentiment triplet can be represented as a rectangular region in a 2D table with boundary tags S and E.
Adopted from BDTF [19] and stated in Section II-A and Fig. 2; the whole method's region detection and classification depend on it.
domain assumption Cross-granularity contrastive learning with Euclidean distance and a fixed margin improves downstream region classification.
Eq. (8) defines the loss; the paper provides no theoretical or empirical justification beyond the ablation, and the fixed margin 1.0 is not tuned.
domain assumption Reported baseline F1 numbers from prior papers are directly comparable to the authors' runs.
Table II sources baselines from [19] and from each original paper; the authors do not rerun them, so comparability of preprocessing, hyperparameters, and decoding is assumed.

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Cite this review

Pith. "Pith review of Boundary-Driven Table-Filling with Cross-Granularity Contrastive Learning for Aspect Sentiment Triplet Extraction." pith.science (2026). https://pith.science/paper/56P4BBXT

@misc{pith2026250201942,
  author       = {Pith},
  title        = {Pith review of: Boundary-Driven Table-Filling with Cross-Granularity Contrastive Learning for Aspect Sentiment Triplet Extraction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/56P4BBXT}},
  note         = {Machine review of arXiv:2502.01942}
}

read the original abstract

The Aspect Sentiment Triplet Extraction (ASTE) task aims to extract aspect terms, opinion terms, and their corresponding sentiment polarity from a given sentence. It remains one of the most prominent subtasks in fine-grained sentiment analysis. Most existing approaches frame triplet extraction as a 2D table-filling process in an end-to-end manner, focusing primarily on word-level interactions while often overlooking sentence-level representations. This limitation hampers the model's ability to capture global contextual information, particularly when dealing with multi-word aspect and opinion terms in complex sentences. To address these issues, we propose boundary-driven table-filling with cross-granularity contrastive learning (BTF-CCL) to enhance the semantic consistency between sentence-level representations and word-level representations. By constructing positive and negative sample pairs, the model is forced to learn the associations at both the sentence level and the word level. Additionally, a multi-scale, multi-granularity convolutional method is proposed to capture rich semantic information better. Our approach can capture sentence-level contextual information more effectively while maintaining sensitivity to local details. Experimental results show that the proposed method achieves state-of-the-art performance on public benchmarks according to the F1 score.

Figures

Figures reproduced from arXiv: 2502.01942 by the authors.

**Figure 2.** An example of the aspect and opinion terms marked regions in the [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** The overview of BTF-CCL. The sentence is encoded by BERT Encoder, enriched with word-level representations via MMCNN, and then contrastive [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.

[1] [17]

Improving span-based aspect sentiment triplet extraction with part-of-speech filtering and contrastive learning,

Q. Li, W. Wen, and J. Qin, “Improving span-based aspect sentiment triplet extraction with part-of-speech filtering and contrastive learning,” Neural Networks, vol. 177, p. 106381, 2024

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Q. Sun, L. Yang, M. Ma, N. Ye, and Q. Gu, “Rethinking aste: A min- imalist tagging scheme alongside contrastive learning,” arXiv preprint arXiv:2403.07342, 2024

work page Pith review arXiv 2024

[3] [19]

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Y . Zhang, Y . Yang, Y . Li, B. Liang, S. Chen, Y . Dang, M. Yang, and R. Xu, “Boundary-driven table-filling for aspect sentiment triplet extrac- tion,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022, pp. 6485–6498

work page 2022

[4] [1]

SemEval-2014 task 4: Aspect based sentiment analysis,

M. Pontiki, D. Galanis, J. Pavlopoulos, H. Papageorgiou, I. Androutsopoulos, and S. Manandhar, “SemEval-2014 task 4: Aspect based sentiment analysis,” in Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014) , P. Nakov and T. Zesch, Eds. Dublin, Ireland: Association for Computational Linguistics, Aug. 2014, pp. 27–35. [Online...

work page 2014

[5] [2]

SemEval-2015 task 12: Aspect based sentiment analysis,

M. Pontiki, D. Galanis, H. Papageorgiou, S. Manandhar, and I. Androutsopoulos, “SemEval-2015 task 12: Aspect based sentiment analysis,” in Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015) , P. Nakov, T. Zesch, D. Cer, and D. Jurgens, Eds. Denver, Colorado: Association for Computational Linguistics, Jun. 2015, pp. 486–495...

work page 2015

[6] [3]

Semeval-2016 task 5: Aspect based sentiment analysis,

M. Pontiki, D. Galanis, H. Papageorgiou, I. Androutsopoulos, S. Man- andhar, M. Al-Smadi, M. Al-Ayyoub, Y . Zhao, B. Qin, O. De Clercq et al. , “Semeval-2016 task 5: Aspect based sentiment analysis,” in International workshop on semantic evaluation , 2016, pp. 19–30

2016

[7] [4]

Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and svm classifier,

A. S. Manek, P. D. Shenoy, and M. C. Mohan, “Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and svm classifier,” World wide web, vol. 20, pp. 135– 154, 2017

work page 2017

[8] [5]

Aspect Term Extraction with History Attention and Selective Transformation

X. Li, L. Bing, P. Li, W. Lam, and Z. Yang, “Aspect term extraction with history attention and selective transformation,” arXiv preprint arXiv:1805.00760, 2018

work page Pith review arXiv 2018