Papers with Deep NLP models
Towards Dynamic Computation Graphs via Sparse Latent Structure (D18-1)
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| Challenge: | Existing approaches to learn latent structure are limited by factorization assumptions or end-to-end differentiability. |
| Approach: | They propose a method that allows for end-to-end learning of latent structure predictors jointly with a downstream predictor. |
| Outcome: | The proposed method allows for unrestricted dynamic graph construction from the global latent structure while maintaining differentiability. |
Retrieval-guided Counterfactual Generation for QA (2022.acl-long)
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| Challenge: | Recent work shows that data augmentation using counterfactuals can help ameliorate this weakness. |
| Approach: | They propose a Retrieve-Generate-Filter technique to generate counterfactuals using QA framework and question generation model trained on original task data. |
| Outcome: | The proposed method improves performance on out-of-domain and challenging evaluation sets over and above existing methods. |