| 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. |
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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. |
Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers (2020.emnlp-main)
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| Challenge: | Existing methods for improving model interpretability require prior information or human annotations as additional inputs. |
| Approach: | They propose a variational word mask method to automatically learn task-specific important words and reduce irrelevant information on classification, which ultimately improves model interpretability. |
| Outcome: | The proposed method improves model prediction accuracy and interpretability on seven datasets. |
Interpretable Rationale Augmented Charge Prediction System (C18-2)
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| 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. |
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. |
DoLFIn: Distributions over Latent Features for Interpretability (2020.coling-main)
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| Challenge: | Existing approaches to interpret neural networks face a trade-off between a model's usefulness and its complexity. |
| Approach: | They propose a novel approach to achieve interpretability that avoids this trade-off by using probability as the central quantity instead of a fixed quantity. |
| Outcome: | The proposed approach outperforms the classical CNN and BiLSTM classifiers on the SST2 and AG-news datasets. |
Self-training with Few-shot Rationalization (2021.emnlp-main)
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| Challenge: | Recent work focused on training largescale and complex neural network models, but they are opaque in terms of their decision-making process. |
| Approach: | They propose a multi-task teacher-student framework for self-training pre-trained language models with limited task-specific labels and annotated rationales. |
| Outcome: | The proposed model improves performance in low-resource settings by making it aware of its rationalized predictions. |
Latent-Variable Generative Models for Data-Efficient Text Classification (D19-1)
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| Challenge: | Generative classifiers offer potential advantages over discriminative classifications, including data efficiency and zero-shot learning. |
| Approach: | They introduce discrete latent variables into generative story to improve classifiers' performance . they empirically characterize performance of their models on six text classification datasets . |
| Outcome: | The proposed model outperforms discriminative and generative classifiers on six text classification datasets. |
Plausible Extractive Rationalization through Semi-Supervised Entailment Signal (2024.findings-acl)
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| Challenge: | Abstract: Large language models are gaining widespread adoption in natural language processing tasks. |
| Approach: | They propose a semi-supervised approach to optimize for plausibility of extracted rationales by using a pre-trained natural language inference model and a supervised NLI predictor. |
| Outcome: | The proposed model outperforms unsupervised models by > 100% on a ERASER dataset. |
Deep Latent Variable Models of Natural Language (D18-3)
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| Challenge: | In this tutorial, we will discuss the challenges of applying neural variational inference to NLP problems. |
| Approach: | The tutorial will cover deep latent variable models in the case where exact inference over the latent variables is tractable. |
| Outcome: | The proposed tutorial will cover deep latent variable models in the case where inference cannot be performed tractably and when it is not . |
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. |