Summary of Explainability in Neural Networks For Natural Language Processing Tasks, by Melkamu Mersha et al.
Explainability in Neural Networks for Natural Language Processing Tasks
by Melkamu Mersha, Mingiziem Bitewa, Tsion Abay, Jugal Kalita
First submitted to arxiv on: 23 Dec 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 The paper presents a study on improving the interpretability of neural networks in natural language processing (NLP) tasks, specifically focusing on multi-layer perceptron (MLP) models trained on text classification tasks. It uses Local Interpretable Model-Agnostic Explanations (LIME) to analyze the contribution of individual features to model predictions, providing insights into their behavior and supporting informed decision-making. While LIME is effective in offering localized explanations, it has limitations in capturing global patterns and feature interactions. The paper highlights these strengths and shortcomings, proposing directions for future work to achieve more comprehensive interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers worked on making neural networks clearer. They used a technique called LIME (Local Interpretable Model-Agnostic Explanations) to help understand how a special kind of neural network (MLP) works when classifying text. By looking at what features contribute most to the predictions, LIME helps make the model more transparent and useful for making decisions. Although LIME does a good job explaining specific parts of the model, it’s not great at showing big patterns or connections between features. The study shows what LIME can do well and what it can’t, suggesting ways to improve its abilities. |
Keywords
» Artificial intelligence » Natural language processing » Neural network » Nlp » Text classification
