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The paper proposes ConFIT, a contrastive learning framework for financial text extraction that programmatically generates hard negatives via a Semantic-Preserving Perturbation (SPP) engine. The SPP engine applies three perturbation strategies—Entity Swaps (using the Loughran–McDonald lexicon and Wikidata), Numerical Sensitivity Adjustments, and Context Reordering—and filters candidates in two stages using perplexity thresholds and NLI entailment scores (Sec 4.1–4.2). ConFIT is evaluated on FiQA (aspect-based sentiment) and SENTiVENT (event extraction) with FinBERT and Llama-3 8B, compared against supervised fine-tuning, zero-shot GPT-4, instruction-tuned baselines, and SimCSE (Sec 5.2). Experiments report training/validation F1 and loss curves, with observations of rapid convergence and overfitting around epoch ~10 (Sec 6, Fig. 1), improved stability under multi-dataset synthetic training (Fig. 2), and a severe anomaly in the Synthetic Multi configuration (training F1 0.611 vs validation F1 0.000; Fig. 3). The paper claims that two-stage filtering is crucial and discusses deployment considerations and failure analyses (Secs 6–7).
Cross‑Modal Consistency: 26/50
Textual Logical Soundness: 14/30
Visual Aesthetics & Clarity: 15/20
Overall Score: 55/100
Detailed Evaluation (≤500 words):
1. Cross‑Modal Consistency
• Major 1: Overfitting claim conflicts with Fig. 1 loss trends. Evidence: “loss … slightly increases—after 10 epochs” (Fig. 1(b) shows monotonic decrease to ~0).
• Major 2: Stability claim for multi‑dataset contradicts Fig. 2 F1. Evidence: “multi‑dataset setup showing superior stability” vs Fig. 2(a) red dashed line ≈0/oscillatory.
• Major 3: Smoother loss in multi‑dataset contradicted. Evidence: “exhibits smoother loss trajectories” vs Fig. 2(b) red lines rise/oscillate to ~1.7.
• Major 4: “Optimal around epoch 10” not supported. Evidence: Fig. 1(c) shows Val F1=1.000 for 10/15/20.
• Minor 1: Figure 1 caption mentions only “Left” and “Middle” but plot has a third pane. Evidence: “Figure 1: (Left)… (Middle)…”.
• Minor 2: Panels lack (a/b/c) labels; text refers by position, causing ambiguity.
• Minor 3: Several image blocks appear without captions near the text, increasing reference ambiguity.
2. Text Logic
• Major 1: Central narrative of overfitting after epoch≈10 is unsupported by Fig. 1, weakening conclusions about early stopping. Evidence: “optimal performance is achieved around epoch 10.”
• Major 2: Claim that multi‑dataset training generalizes better is contradicted by zero validation F1 (catastrophic failure). Evidence: Fig. 3 shows “Synthetic Multi” Val F1=0.000.
• Minor 1: Results sections assert “promising improvements,” but no quantitative comparison vs baselines (FinBERT, Llama‑3, SimCSE, GPT‑4) is shown.
• Minor 2: Evaluation mentions significance testing, yet no tests or p‑values are reported.
3. Figure Quality
• Image‑first synopsis
– Figure 1: (a) F1 vs epochs (train/val; legends per epoch). Trend: all reach 1.0 early. (b) Loss vs epochs: both losses fall to ~0 by ~10 epochs. (c) Bar: final Val F1=1.000 for 10/15/20. Synopsis: baseline shows perfect scores; no overfitting visible.
– Figure 2: (a) F1 single vs multi synthetic; multi‑val unstable near 0. (b) Loss single vs multi; multi losses high/rising. Synopsis: multi‑dataset pipeline unstable.
– Figure 3: Bars of final F1 across setups; “Synthetic Multi” anomaly (Train 0.611, Val 0.000).
– Figure 4: (a) F1 curves single vs multi domain; both saturate at 1.0. (b) Loss curves similar; (c) Bars all 1.0. Synopsis: domain‑specific variants look equivalent.
• No Major issues found.
• Minor 1: Missing panel labels (a/b/c) across all multi‑pane figures.
• Minor 2: Some legends and top annotations are small but legible; colours consistent but not clearly described in captions.
• Figure‑alone test: Mostly pass due to titles/legends; Fig. 1(c) purpose unclear without caption (Minor).
Key strengths:
Key weaknesses:
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This paper introduces ConFIT, a knowledge-guided contrastive learning framework tailored for financial information extraction. The authors aim to address the challenges of multi-entity sentiment attribution and numerical sensitivity inherent in financial text analysis. ConFIT leverages a Semantic-Preserving Perturbation (SPP) engine to generate hard negative samples, incorporating domain-specific knowledge from the Loughran-McDonald lexicon and Wikidata. The framework employs a two-stage filtering process using perplexity and Natural Language Inference (NLI) to ensure the quality of these negative samples. The core idea is to train language models to better distinguish subtle differences in financial statements by contrasting original statements with these carefully crafted perturbations. The authors evaluate ConFIT on two financial datasets, FiQA and SENTiVENT, using FinBERT and Llama-3 8B as base models. The experimental results, presented through loss curves and F1 scores, suggest that ConFIT can improve model performance on these tasks. However, the paper also acknowledges the challenges in achieving robust and consistent improvements, particularly in multi-dataset scenarios. The authors emphasize the need for further research to address these limitations and to enhance the practical applicability of the framework. While the paper presents a promising approach to financial information extraction, it also highlights the complexities of applying contrastive learning in this domain and the need for more rigorous evaluation and analysis.
I find the core idea of using a knowledge-guided contrastive learning framework for financial text extraction to be quite promising. The authors' attempt to address the challenges of multi-entity sentiment attribution and numerical sensitivity through a Semantic-Preserving Perturbation (SPP) engine is a notable strength. The SPP engine, which generates hard negative samples by perturbing financial statements while preserving their semantic meaning, is a novel approach. The integration of domain knowledge sources, such as the Loughran-McDonald lexicon and Wikidata, to guide the perturbation process is a valuable contribution. This approach allows the model to learn from more nuanced examples, potentially leading to better generalization. Furthermore, the two-stage filtering process, using perplexity and NLI, is a sensible way to ensure the quality of the generated negative samples. This helps to remove trivial or unrealistic negatives, focusing the contrastive learning on more meaningful distinctions. The authors' choice to evaluate the framework on two different financial datasets, FiQA and SENTiVENT, and with two different base models, FinBERT and Llama-3 8B, demonstrates a commitment to assessing the framework's generalizability. The inclusion of a zero-shot GPT-4 baseline also provides a useful point of comparison. While the paper has significant weaknesses, the core concept of knowledge-guided contrastive learning with semantic-preserving perturbations is a valuable contribution to the field of financial NLP.
After a thorough examination of the paper, I have identified several significant weaknesses that undermine the overall impact of the work. Firstly, the paper suffers from a lack of clarity in its writing and presentation. The experimental results, in particular, are not presented in a way that is easy to understand. For instance, the analysis of overfitting in the 'Baseline Analysis and Hyperparameter Tuning' section relies on Figure 1, which is described as showing F1 scores and loss curves, but the figure itself is not included in the provided text. This makes it difficult to verify the authors' claims. Furthermore, the paper uses terms like 'Synthetic Single' and 'Synthetic Multi' without clear definitions, making it hard to understand the experimental setup. This lack of clarity extends to the method description as well. For example, the 'Entity Swaps' component of the SPP engine mentions using the Loughran-McDonald lexicon and Wikidata, but it does not specify how these resources are used to identify and replace entities. The paper states that entities are replaced with 'their financial domain equivalents,' but it does not explain how these equivalents are determined. Similarly, the 'Numerical Sensitivity Adjustments' component introduces a sensitivity parameter, ∈, but it does not provide a detailed explanation of how this parameter is chosen or what its impact is on the generated perturbations. The paper also lacks concrete examples of how these perturbations are applied to real financial statements. This lack of detail makes it difficult to understand the practical implementation of the proposed method. The paper also lacks a clear task definition. While the datasets are mentioned in the 'Experimental Setup' section, the specific tasks being addressed are not explicitly stated at the beginning of the paper. The tasks are later clarified as aspect-based sentiment analysis for FiQA and event extraction for SENTiVENT, but this lack of initial clarity makes it difficult to understand the context of the experiments. The paper also suffers from a lack of comparative baselines. While the 'Model Architectures and Baselines' section lists several baselines, the results of these baselines are not presented in a table, making it difficult to compare the performance of ConFIT with existing methods. The paper also lacks a detailed analysis of the experimental results. For example, the 'Baseline Analysis and Hyperparameter Tuning' section provides some analysis of training dynamics, but it does not offer a detailed breakdown of performance on different aspects or entities within the datasets. The paper also does not include a dedicated 'Limitations' section within the main body, which would have been beneficial for a more balanced assessment of the framework's capabilities. Finally, the paper's claims about addressing the challenges of multi-entity sentiment attribution and numerical sensitivity are not fully supported by the experimental results. While the paper mentions these challenges in the introduction, the experiments do not provide a detailed analysis of how ConFIT specifically addresses these issues. The paper also does not provide a detailed analysis of the computational cost of the proposed method, which is an important consideration for practical applications. The lack of a clear definition of 'hard negatives' and the absence of a detailed explanation of how the NLI filtering is performed further weaken the paper's claims. The paper also does not provide a clear explanation of how the perplexity thresholds are determined. These weaknesses, taken together, significantly undermine the paper's credibility and impact. The lack of clarity, detail, and rigorous evaluation make it difficult to assess the true value of the proposed framework.
To address the identified weaknesses, I recommend several concrete improvements. First and foremost, the authors need to significantly improve the clarity and presentation of their work. This includes providing clear definitions for all terms and concepts, especially those related to the experimental setup and the SPP engine. The paper should include a detailed explanation of how the Loughran-McDonald lexicon and Wikidata are used to identify and replace entities in the 'Entity Swaps' component. The authors should also provide a more detailed explanation of how the sensitivity parameter, ∈, is chosen and what its impact is on the generated perturbations in the 'Numerical Sensitivity Adjustments' component. The paper should also include concrete examples of how these perturbations are applied to real financial statements. The authors should also provide a clear and concise definition of the tasks being addressed at the beginning of the paper, rather than burying them in the experimental setup section. The paper should also include a table comparing the performance of ConFIT with the listed baselines, including specific metrics like precision, recall, and F1-score. The authors should also provide a more detailed analysis of the experimental results, including a breakdown of performance on different aspects or entities within the datasets. The paper should also include a dedicated 'Limitations' section within the main body, which would provide a more balanced assessment of the framework's capabilities. The authors should also provide more detailed evidence to support their claims about addressing multi-entity sentiment attribution and numerical sensitivity. This could include specific examples or analyses that demonstrate how ConFIT handles these challenges. The authors should also include a detailed analysis of the computational cost of the proposed method, including the time and resources required for training and inference. The authors should also provide a clear definition of 'hard negatives' and a detailed explanation of how the NLI filtering is performed, including the specific NLI model used and the thresholds applied. The authors should also provide a clear explanation of how the perplexity thresholds are determined. The authors should also consider including ablation studies to evaluate the contribution of each component of the framework. Finally, the authors should carefully proofread the paper to ensure that it is free of grammatical errors and typos. By addressing these issues, the authors can significantly improve the clarity, rigor, and impact of their work.
Based on my analysis, I have several questions that I believe are crucial for understanding the paper's contributions and limitations. First, could the authors provide a more detailed explanation of how the Loughran-McDonald lexicon and Wikidata are used to identify and replace entities in the 'Entity Swaps' component? Specifically, what criteria are used to determine which entities are replaced, and how are their 'financial domain equivalents' identified? Second, could the authors provide a more detailed explanation of how the sensitivity parameter, ∈, is chosen in the 'Numerical Sensitivity Adjustments' component? What is the rationale behind the chosen value, and what is the impact of varying this parameter on the generated perturbations and the model's performance? Third, could the authors provide concrete examples of how the perturbations generated by the SPP engine are applied to real financial statements? This would help to clarify the practical implementation of the proposed method. Fourth, could the authors provide a more detailed analysis of the experimental results, including a breakdown of performance on different aspects or entities within the FiQA and SENTiVENT datasets? This would help to understand the strengths and weaknesses of the proposed framework in different scenarios. Fifth, could the authors provide a more detailed explanation of how the perplexity thresholds are determined in the two-stage filtering process? What is the rationale behind the chosen values, and what is the impact of varying these thresholds on the quality of the generated negative samples? Sixth, could the authors provide a more detailed explanation of how the NLI filtering is performed, including the specific NLI model used and the thresholds applied? What is the impact of varying these thresholds on the quality of the generated negative samples? Finally, could the authors provide a more detailed analysis of the computational cost of the proposed method, including the time and resources required for training and inference? This would be important for assessing the practical applicability of the framework.