GAProtoNet: A Multi-head Graph Attention-based Prototypical Network for Interpretable Binary Text Classification
Authors: Ximing Wen, Wenjuan Tan, Rosina Weber


Reviewer paper summaries: This paper proposed a prototype-based network for text classification. The input is compared with the prototypes from the training set to construct a graph. The edge weights of the graph are computed by the similarities between text embeddings. The prototypical representation of the input is computed by the multi-head attention over the graph nodes. The final classification is taken by learning an MLP later over the prototypical representation. The method is evaluated on three binary classification tasks and proposed method shows comparable performance to baselines.
Summary of strengths: The goal of building inherently interpretable models is well-motivated.
Summary of weaknesses: 1. **The proposed network is not inherently interpretable.** I respect the effort to build interpretable-by-design methods for language tasks. Unfortunately, the proposed method is not interpretable from my perspective. An inherently interpretable model needs to satisfy at least: (1) the model's decision needs to be faithful to the explanations; (2) The model's decision process needs to be simple enough for humans to understand. This paper mainly fails on the second point. Here are the reasons:

* The prototype representation $z$ is not interpretable. Compared to ProtoPNet [1], where the input to the decision layer represents the similarities to all prototypes, each element in that representation can be understood by humans. However, this work did too many operations to obtain the prototypical representation. I agree the edge weights have semantics, but after all the operations (aggregation, attention, concatenation, etc.), I have no idea what the meaning of each element in vector $z$ is.

* The MLP layer makes the whole system a black box. In most inherently interpretable methods [1, 2], the final layer is usually a linear layer (single layer or decision tree), which can be understood by humans. This work uses multiple layers, which makes the system behave like a black box. It is very likely the model learned some behaviors that humans cannot comprehend. 

    Although Figure 2 includes some visualized examples, I don't think they faithfully represent the model's decision process. Those examples are more like post-hoc explanations since you can always find similar text for the input, even for a black-box model.

2. **Generalization to more challenging tasks.** This paper only evaluates three binary classification methods, which are too easy, and almost all models achieve nearly 100% performance. Will the proposed method still work for multi-way classification or more challenging tasks? One motivation for inherently interpretable methods is to apply them in critical domains. Only showing results on those fake tasks, such as restaurant reviews, makes me feel the author did not consider motivation seriously.

3. **Lack of human evaluation for interpretability.** This is also a missing part of this paper. To demonstrate the interpretability, you need to show your model's behavior to humans and see if they agree with them.

[1] This Looks Like That: Deep Learning for Interpretable Image Recognition. NeurIPS 2019.
[2] Concept Bottleneck Models. ICML 2020.
Comments, suggestions and typos: Simply the method. Is it necessary to have all those complicated operations? How about directly using the similarities with prototypes as input to a linear layer?
Overall assessment scores: 2
Metareview score: 3
Metareview: This paper presents GAProtoNet, a novel white-box Multi-head Graph Attention-based Prototypical Network designed to explain the decisions of text classification models built with language model (LM) encoders. By treating input vectors and prototypes as graph nodes, GAProtoNet employs multi-head graph attention to selectively construct edges between the input node and prototype nodes. The model makes a decision based on the edge weights during inference, enabling interpretability of the decision-making. The authors demonstrate the model's effectiveness through empirical studies on several benchmarks, showing that GAProtoNet enhances interpretability without sacrificing accuracy.

Reviewers recognize the promise of GAProtoNet in capturing the relationships between input vectors and prototypes, as well as in enhancing the interpretability of black-box LMs without compromising accuracy. However, they have raised concerns regarding the validity of the methodology, the true inherent interpretability of the model, and the lack of comprehensive evaluation on more challenging tasks or through human evaluations.

While the authors’ responses have clarified several issues and the additional experiments would be beneficial to include in the paper, I think that the reviewers’ concerns about the methodology and evaluation have not been fully addressed. As such, I recommend another round of revision to address these points more thoroughly.
Summary of reasons to publish: -	The integration of multi-head graph attention for constructing edges between input and prototypes offers a novel and promising direction for interpretability.
-	The empirical results are strong, indicating that GAProtoNet performs competitively or even superior to baseline models while enhancing interpretability. 
-	The paper is well-organized and clearly motivated by the need for inherently interpretable models.