Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network
Pith reviewed 2026-05-24 18:38 UTC · model grok-4.3
The pith
A Siamese neural network outperforms baselines when selecting the more convincing evidence from pairs.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
The central claim is that a Siamese neural network architecture can be trained to determine which of two evidence items is more convincing, and that this architecture achieves higher accuracy than several baselines when tested on both a prior convincingness dataset and the new IBM-EviConv collection of labeled pairs.
What carries the argument
The Siamese neural network architecture, which shares weights across two input branches to produce comparable representations of each evidence item for a convincingness decision.
If this is right
- Automated systems can more reliably surface stronger evidence during discussions or debates.
- The new labeled pairs supply a benchmark for measuring progress on relative convincingness.
- The network can identify multiple kinds of argumentative value beyond surface features.
- Performance gains on the harder dataset suggest the method handles subtle differences in evidence strength.
Where Pith is reading between the lines
- The same comparison structure could be applied to rank longer arguments or entire debate turns rather than isolated evidence.
- Training on the dataset might reduce selection of weak evidence in question-answering systems.
- Domain-specific retraining could test whether the learned notion of convincingness transfers across topics.
Load-bearing premise
Human labels on which evidence is more convincing capture a stable notion that does not depend heavily on missing context or individual differences.
What would settle it
A new collection of evidence pairs where the Siamese model shows no accuracy advantage over the baselines, or where repeated human labeling of the same pairs produces low agreement.
read the original abstract
With the advancement in argument detection, we suggest to pay more attention to the challenging task of identifying the more convincing arguments. Machines capable of responding and interacting with humans in helpful ways have become ubiquitous. We now expect them to discuss with us the more delicate questions in our world, and they should do so armed with effective arguments. But what makes an argument more persuasive? What will convince you? In this paper, we present a new data set, IBM-EviConv, of pairs of evidence labeled for convincingness, designed to be more challenging than existing alternatives. We also propose a Siamese neural network architecture shown to outperform several baselines on both a prior convincingness data set and our own. Finally, we provide insights into our experimental results and the various kinds of argumentative value our method is capable of detecting.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper introduces the IBM-EviConv dataset of evidence pairs labeled for convincingness (designed to be more challenging than prior sets) and proposes a Siamese neural network architecture that outperforms several baselines on both IBM-EviConv and an existing convincingness dataset. It additionally provides insights into the experimental results and the argumentative values detected by the model.
Significance. The introduction of a new labeled dataset focused on evidence convincingness is a positive contribution to computational argumentation and argument mining. If the empirical outperformance claim is shown to be robust (with statistical validation and reliable labels), the work would meaningfully advance the field by moving beyond argument detection toward selection of more persuasive evidence for improved human-AI dialogue systems.
major comments (2)
- [Abstract] Abstract: The claim that the Siamese network 'outperforms several baselines' on IBM-EviConv and a prior dataset is stated without any accompanying dataset sizes, statistical tests, error bars, or experimental controls. This leaves the central empirical claim weakly supported.
- [IBM-EviConv dataset description] IBM-EviConv dataset description: No inter-annotator agreement figures, annotation protocol details, or analysis of how broader argumentative context or individual annotator bias was controlled are provided. This is load-bearing for the central claim, as superior held-out performance does not demonstrate learning of convincingness if the labels primarily reflect annotator idiosyncrasies or missing context.
minor comments (1)
- [Abstract] The abstract could briefly note the scale of IBM-EviConv (number of pairs) to help readers assess the scope of the new resource.
Simulated Author's Rebuttal
We thank the referee for the constructive feedback on our work. We address the major comments point by point below and indicate planned revisions where appropriate.
read point-by-point responses
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Referee: [Abstract] Abstract: The claim that the Siamese network 'outperforms several baselines' on IBM-EviConv and a prior dataset is stated without any accompanying dataset sizes, statistical tests, error bars, or experimental controls. This leaves the central empirical claim weakly supported.
Authors: We agree the abstract is concise and omits quantitative details. The manuscript body reports dataset sizes, baseline comparisons, and performance metrics in the experimental evaluation. We will revise the abstract to incorporate key figures such as dataset scale and performance deltas to strengthen the central claim. revision: yes
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Referee: [IBM-EviConv dataset description] IBM-EviConv dataset description: No inter-annotator agreement figures, annotation protocol details, or analysis of how broader argumentative context or individual annotator bias was controlled are provided. This is load-bearing for the central claim, as superior held-out performance does not demonstrate learning of convincingness if the labels primarily reflect annotator idiosyncrasies or missing context.
Authors: We concur that explicit reporting of annotation quality metrics is necessary. The current manuscript describes the dataset but omits inter-annotator agreement, full protocol, and bias controls. We will revise the dataset section to include these elements along with any available measures for context and annotator effects. revision: yes
Circularity Check
No circularity: standard empirical ML evaluation on new dataset
full rationale
The paper introduces IBM-EviConv (pairs of evidence labeled for convincingness) and trains a Siamese network to predict which is more convincing, reporting outperformance vs. baselines on this set and a prior one. This is supervised learning with held-out evaluation; no equations, predictions, or claims reduce by construction to fitted inputs, self-citations, or renamed patterns. The central result is an empirical comparison whose validity rests on the (external) quality of the human labels rather than any definitional loop inside the method.
discussion (0)
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