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. |
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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. |
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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. |
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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. |
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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. |
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Interventional Rationalization (2023.emnlp-main)
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| Challenge: | Existing methods for rationalization use spurious correlations in data to compose rationales and make predictions. |
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MGR: Multi-generator Based Rationalization (2023.acl-long)
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| Challenge: | Existing approaches to explain NLP models have two key challenges: spurious correlation and degeneration. |
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Principled Content Selection to Generate Diverse and Personalized Multi-Document Summaries (2025.acl-long)
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| Challenge: | Large language models exhibit the _”lost in the middle” phenomenon when they are unevenly attending to different parts of the provided context. |
| Approach: | They propose principled content selection as a way to increase source coverage . they use determinantal point processes to prioritize diverse content . |
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Zero-Shot Rationalization by Multi-Task Transfer Learning from Question Answering (2020.findings-emnlp)
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| Challenge: | Existing methods to extract rationales from input text are difficult and impractical. |
| Approach: | They propose a method that leverages multi-task learning and transfer learning to generate rationales through question answering in a zero-shot fashion. |
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SPECTRA: Sparse Structured Text Rationalization (2021.emnlp-main)
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| Challenge: | Sparse attention mechanisms are a deterministic alternative, but they lack a way to regularize rationale extraction. |
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CREST: A Joint Framework for Rationalization and Counterfactual Text Generation (2023.acl-long)
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| Challenge: | Existing methods for analyzing and training NLP models have not been integrated to combine their complementary advantages. |
| Approach: | They introduce a framework for selective rationalization and counterfactual text generation that leverages CREST to regularize selective rationales and a loss function that regularizes selective rationals. |
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