Papers with zero-shot learning setting
Event Extraction by Answering (Almost) Natural Questions (2020.emnlp-main)
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| Challenge: | Existing work in event argument extraction relies heavily on entity recognition as a preprocessing/concurrent step, causing error propagation. |
| Approach: | They propose a question answering task that extracts event arguments in an end-to-end manner. |
| Outcome: | The proposed framework outperforms prior work on the ACE 2005 task on event argument extraction. |
MAFALDA: A Benchmark and Comprehensive Study of Fallacy Detection and Classification (2024.naacl-long)
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| Challenge: | Fallacy classification is a task of broad importance due to advances in deep learning and availability of more data. |
| Approach: | They propose a new annotation scheme tailored for subjective NLP tasks and a method designed to handle subjectivity. |
| Outcome: | The proposed approach integrates existing fallacy classification datasets with new ones. |
Contrastive Training Improves Zero-Shot Classification of Semi-structured Documents (2023.findings-acl)
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| Challenge: | Xu et al., 2020 focus on semi-structured document classification in a zero-shot setting . positional, layout, and style information play a vital role in interpreting such documents . |
| Approach: | They propose a matching-based approach that relies on a pairwise contrastive objective for pretraining and fine-tuning. |
| Outcome: | The proposed method significantly improves Macro F1 in the zero-shot learning setting. |