Advancing Interpretability in Text Classification through Prototype Learning (2410.17546v2)

Abstract: Deep neural networks have achieved remarkable performance in various text-based tasks but often lack interpretability, making them less suitable for applications where transparency is critical. To address this, we propose ProtoLens, a novel prototype-based model that provides fine-grained, sub-sentence level interpretability for text classification. ProtoLens uses a Prototype-aware Span Extraction module to identify relevant text spans associated with learned prototypes and a Prototype Alignment mechanism to ensure prototypes are semantically meaningful throughout training. By aligning the prototype embeddings with human-understandable examples, ProtoLens provides interpretable predictions while maintaining competitive accuracy. Extensive experiments demonstrate that ProtoLens outperforms both prototype-based and non-interpretable baselines on multiple text classification benchmarks. Code and data are available at \url{https://anonymous.4open.science/r/ProtoLens-CE0B/}.

• The paper introduces ProtoLens, a novel prototype-based model that improves interpretability in text classification through fine-grained sub-sentence analysis.
• The model utilizes a prototype-aware span extraction via DPGMM and a prototype alignment mechanism to ensure semantic clarity in its predictions.
• Empirical results show superior performance, including a 0.903 accuracy on the IMDB dataset, highlighting both effectiveness and transparency.

Advancing Interpretability in Text Classification through Prototype Learning

This essay discusses a paper that introduces ProtoLens, a novel prototype-based model designed to enhance interpretability in text classification tasks. Traditionally, deep neural networks (DNNs) have excelled in tasks such as text classification, sentiment analysis, and question answering. However, a significant drawback of these models is their "black-box" nature, which often hinders their application in domains where model interpretability and transparency are critical. ProtoLens addresses these challenges by offering a more granular approach to interpretability.

Key Contributions and Methodology

ProtoLens innovatively incorporates two core modules to incorporate interpretability directly within its architecture:

1. Prototype-aware Span Extraction Module: Using a Dirichlet Process Gaussian Mixture Model (DPGMM), this module identifies relevant sub-sentence text spans associated with prototypes. This fine-grained extraction allows the model to capture specific details within a text, improving interpretability over models that analyze whole sentences.
2. Prototype Alignment Mechanism: This mechanism ensures that the learned prototypes stay semantically meaningful by adjusting their embeddings based on representative text samples throughout the training process. This guarantees that prototypes are aligned with human-understandable text, which is crucial for maintaining transparency.

The model's structure is designed to process text inputs by comparing them to learned prototypes, akin to human reasoning via analogies. This involves a comprehensive similarity assessment between extracted text spans and prototype embeddings, contributing to interpretable predictions.

Empirical Results

The paper reports extensive empirical evaluations across various text classification benchmarks, such as IMDB, Amazon, Yelp, Hotel, and Steam datasets. ProtoLens not only demonstrated superior classification accuracy, outperforming both prototype-based and non-interpretable baselines, but also delivered user-friendly explanations for its predictions. Key findings include:

• ProtoLens achieves an accuracy of 0.903 on the IMDB dataset, decisively outperforming other methods like MPNet (0.846) and ProSeNet (0.863).
• Ablation studies reveal the critical role of Prototype Alignment and Diversity Loss in maintaining high performance and interpretability, as their removal led to significant accuracy declines.

Theoretical and Practical Implications

Theoretically, ProtoLens advances prototype learning by moving beyond instance-level interpretations to sub-sentence granularity, bridging a gap in current interpretability research. This fine-grained interpretability offers robust and detailed insights suitable for complex text data analysis.

Practically, ProtoLens is positioned as a suitable model for high-stakes applications requiring transparency and user trust. Its ability to provide interpretable outputs could be pivotal in fields like healthcare and legal services, where accountability is crucial.

Looking Forward

Future developments could aim to adapt ProtoLens to more complex NLP tasks, such as machine translation or summarization, potentially requiring architectural modifications. Furthermore, integrating methods to detect and counter biases inherent in training data could enhance the model's robustness and fairness.

Overall, ProtoLens represents an important step towards more interpretable AI models, maintaining high performance without compromising on transparency, which could facilitate broader acceptance and application of AI in critical areas.