| Challenge: | Existing methods for rationalization use spurious correlations in data to compose rationales and make predictions. |
| Approach: | They propose a method to discover the causal rationales by using a structural causal model. |
| Outcome: | The proposed method is based on the causal theory and validates on three real-world datasets. |
Similar Papers
Unsupervised Selective Rationalization with Noise Injection (2023.acl-long)
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| Challenge: | Unsupervised selective rationalization produces rationales alongside predictions, but does not ensure that the rationale contains a plausible explanation for the prediction. |
| Approach: | They propose a technique that injects noise between a rationale generator and a predictor to limit generation of implausible rationales. |
| Outcome: | The proposed method achieves significant improvements in plausibility and task accuracy over the state-of-the-art models while maintaining or improving model faithfulness. |
Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control (D19-1)
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| Challenge: | Selective rationalization is a common mechanism to ensure that predictive models reveal how they use any available features. |
| Approach: | They propose a co-operative method which uses introspection to explicitly predict and incorporate the outcome into the selection process. |
| Outcome: | The proposed model maintains high predictive accuracy and leads to comprehensive rationales. |
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. |
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. |
Exploring Distantly-Labeled Rationales in Neural Network Models (2021.acl-long)
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| Challenge: | Existing methods focus on distantly-labeled rationales, ignoring the potential important non-rationale words and not distinguishing the importance of different rationale words. |
| Approach: | They propose two novel auxiliary loss functions to make better use of distantly-labeled rationales, which encourage models to maintain their focus on important words beyond labeled rationals (PINs) and alleviate redundant training on non-helpful rationale (NoIRs). |
| Outcome: | The proposed methods outperform existing methods on two representative classification tasks while maintaining the ability to spread focus to other unlabeled important words. |
AGR: Reinforced Causal Agent-Guided Self-explaining Rationalization (2024.acl-short)
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| Challenge: | Existing rationalization approaches are susceptible to degeneration due to lack of effective control over the learning direction of the model during training. |
| Approach: | They propose an agent-guided rationalization approach that guides the next step of the model based on its current training state. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on BeerAdvocate and HotelReview datasets. |
Interlocking-free Selective Rationalization Through Genetic-based Learning (2025.acl-long)
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| Challenge: | Existing approaches to selective rationalization suffer from interlocking, a phenomenon known as interlock. |
| Approach: | They propose a genetically-based disjoint training architecture for selective rationalization that avoids interlocking by performing genetic global search. |
| Outcome: | The proposed model outperforms state-of-the-art models on a synthetic and real-world benchmark. |
Rationales for Sequential Predictions (2021.emnlp-main)
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| Challenge: | Sequence models produce accurate predictions, but their decision making processes are hard to explain. |
| Approach: | They propose an efficient algorithm to approximate sequential objective by identifying the most faithful rationales. |
| Outcome: | The proposed algorithm is best at optimizing the sequential objective and provides the most faithful rationales. |
Does Self-Rationalization Improve Robustness to Spurious Correlations? (2022.emnlp-main)
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| Challenge: | Rationalization is fundamental to human reasoning and learning. |
| Approach: | They evaluate robustness to spurious correlations in encoder-decoder and decoder-only models . authors say explanations can come at the cost of robustness . |
| Outcome: | The proposed model outputs are more interpretable and easier to interact with for end-users than nonrationalizing models. |
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. |