| Challenge: | Existing studies treat charge prediction as a text classification problem, but in the field of justice, every decision may be a matter of life and death. |
| Approach: | They propose to extract readable rationales from text and then create a rationale augmented classification model to enhance the prediction accuracy. |
| Outcome: | The proposed system can extract readable rationales in a high consistency with manual annotation and is comparable with the attention model in prediction accuracy. |
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Interpretable Neural Predictions with Differentiable Binary Variables (P19-1)
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| Challenge: | Neural networks are bringing incredible performance gains on text classification tasks, but they also require interpretability. |
| Approach: | They propose a latent model that selects a rationale and a classifier that learns from the words in the rationale alone. |
| Outcome: | The proposed model can predict expected value of penalties without REINFORCE and can be directly optimised towards a pre-specified text selection rate. |
Model Interpretability and Rationale Extraction by Input Mask Optimization (2023.findings-acl)
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| Challenge: | Existing methods for creating explanations for black-box models struggle with deriving easily interpretable explanations. |
| Approach: | They propose a model-agnostic method to generate extractive explanations for neural network predictions using masking parts of the input that the model does not consider indicative of the respective class. |
| Outcome: | The proposed method achieves state-of-the-art results in a paragraph-level rationale extraction task, showing that this task can be performed without training a specialized model. |
RANCC: Rationalizing Neural Networks via Concept Clustering (2020.coling-main)
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| Challenge: | Existing models that construct explanations concurrently with classification predictions are opaque. |
| Approach: | They propose a self-explainable model for Natural Language Processing (NLP) text classification tasks . they extract a rationale from the text and use it to predict a concept of interest . |
| Outcome: | The proposed model can be compressed without complicated compression techniques. |
Learning to Extract Rational Evidence via Reinforcement Learning for Retrieval-Augmented Generation (2026.findings-acl)
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| Challenge: | Retrieval-Augmented Generation (RAG) is effective in Large Language Models (LLMs). However, retrieval noises undermine the quality of LLMs’ generation, necessitating the development of denoising mechanisms. |
| Approach: | They propose a model which integrates reasoning and extracting into one unified trajectory, followed by knowledge token masking to avoid information leakage. |
| Outcome: | Extensive experiments on five benchmark datasets show the superiority of EviOmni, which provides compact and high-quality evidence, enhances the accuracy of downstream tasks, and supports both traditional and agentic RAG systems. |
Ranking-Constrained Learning with Rationales for Text Classification (2022.findings-acl)
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| Challenge: | Existing approaches to text classification use labels and rationales as ranking constraints. |
| Approach: | They propose a ranking-constrained loss function that combines cross-entropy loss with ranking losses as rationale constraints to speed up deep learning models with limited training data. |
| Outcome: | The proposed approach outperforms baselines on three human-annotated datasets and shows that it is more efficient than existing approaches. |
Learning to Faithfully Rationalize by Construction (2020.acl-main)
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| Challenge: | Neural models dominate NLP but it remains difficult to know why they make specific predictions for sequential text inputs. |
| Approach: | They propose a model to produce faithful rationales for neural text classification by defining independent snippet extraction and prediction modules. |
| Outcome: | The proposed model produces faithful explanations even when the model is complex and complex. |
Towards Explainable NLP: A Generative Explanation Framework for Text Classification (P19-1)
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| Challenge: | Existing approaches for explainable machine learning systems focus on interpreting outputs or connections between inputs and outputs. |
| Approach: | They propose a generative explanation framework that learns to make classification decisions and generates fine-grained explanations at the same time. |
| Outcome: | The proposed framework surpasses all baselines on two datasets and generates concise explanations at the same time. |
Unveiling Decision-Making in LLMs for Text Classification : Extraction of influential and interpretable concepts with Sparse Autoencoders (2026.findings-eacl)
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| Challenge: | Concept-based explanations for large language models are not well understood in text classification. |
| Approach: | They propose a model with a specialized classifier head and activation rate sparsity loss for sentence classification . they compare it to existing models with HI-Concept and ConceptShap . |
| Outcome: | The proposed model improves both the causality and interpretability of the extracted features. |
Can Rationalization Improve Robustness? (2022.naacl-main)
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| Challenge: | Existing models that generate rationales before making predictions can ignore noise or adversarially added text by simply masking it out of the generated rationale. |
| Approach: | They propose to use a 'rationalizethen-predict' framework to generate subsets of input to generate rationales and then make predictions using them. |
| Outcome: | The proposed models improve robustness over AddText attacks while struggling in certain scenarios. |
Improve Interpretability of Neural Networks via Sparse Contrastive Coding (2022.findings-emnlp)
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| Challenge: | XAI has achieved remarkable advances, but few efforts have been devoted to solving the problem. |
| Approach: | They propose a model-agnostic explanation method termed Sparse Contrastive Coding . they use model-based explanations to explain the black-box in a more model-oriented way . |
| Outcome: | The proposed method outperforms five state-of-the-art methods in interpretability and classification metrics. |