Summary of Advancing Interpretability in Text Classification Through Prototype Learning, by Bowen Wei et al.
Advancing Interpretability in Text Classification through Prototype Learning
by Bowen Wei, Ziwei Zhu
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel prototype-based model, called ProtoLens, is proposed to provide fine-grained, sub-sentence level interpretability for text classification tasks. This approach 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. The model outperforms both prototype-based and non-interpretable baselines on multiple text classification benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ProtoLens is a new way to understand why deep neural networks are making certain decisions when classifying text. It works by identifying important parts of the text that match what it’s learned, and then shows how these parts relate to real-life examples. This makes it easier for people to see why certain decisions were made, which is important in situations where transparency is crucial. |
Keywords
» Artificial intelligence » Alignment » Text classification
