Papers with English-language datasets
Few-Shot Self-Rationalization with Natural Language Prompts (2022.findings-naacl)
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| Challenge: | Existing models that generate free-text explanations for tasks are limited by human-written explanations. |
| Approach: | They propose to use a standardized collection of natural language prompts to create a model that generates free-text explanations for tasks. |
| Outcome: | The proposed model can predict task labels and generate free-text explanations for predictions . plausibility of human explanations is 76%, while human explanation is 51% . |
Data and Representation for Turkish Natural Language Inference (2020.emnlp-main)
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| Challenge: | Large annotated datasets in NLP are overwhelmingly in English . obtaining new annotation resources for each task in each language would be prohibitively expensive . |
| Approach: | They propose to use machine translation to translate large annotated datasets into Turkish . they find that in-language embeddings are essential and morphological parsing can be avoided . |
| Outcome: | The proposed model trains on human-translated evaluation sets. |