Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention (2021.emnlp-main)
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| Challenge: | Recent supervised ED approaches have achieved promising performance but require large number of manually annotated event data. |
| Approach: | They propose to overfit the trigger confounder of the context and the result . they propose to intervene on the context via backdoor adjustment during training . |
| Outcome: | The proposed method significantly improves the FSED on ACE05 and MAVEN datasets. |
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| Challenge: | Existing methods to perform few-shot named entity recognition are limited and overfitting is caused by the spurious correlation resulting from the bias in selecting a few samples. |
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Prototype-based Prompt-Instance Interaction with Causal Intervention for Few-shot Event Detection (2024.lrec-main)
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| Challenge: | Few-shot Event Detection (FSED) requires limited labeled data and expensive manual labeling. |
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Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning (2021.emnlp-main)
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Jianguo Zhang, Trung Bui, Seunghyun Yoon, Xiang Chen, Zhiwei Liu, Congying Xia, Quan Hung Tran, Walter Chang, Philip Yu
| Challenge: | Existing methods address few-shot intent detection tasks from two perspectives: data augmentation and task-adaptive training with pre-trained models. |
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Class-Incremental Few-Shot Event Detection (2024.lrec-main)
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| Challenge: | Existing methods to deal with new class of events with only a few labeled instances are challenging . old knowledge forgetting and new class overfitting are two problems in this task. |
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The Art of Prompting: Event Detection based on Type Specific Prompts (2023.acl-short)
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| Challenge: | Experimental results show that a well-defined and comprehensive description of event types can significantly improve event detection performance when the annotations are limited. |
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Few-shot Event Detection: An Empirical Study and a Unified View (2023.acl-long)
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| Challenge: | Extensive studies have been carried out on fewshot event detection (ED) however, there are noticeable discrepancies among existing methods from three aspects. |
| Approach: | They propose a unified view of ED models and a better unified baseline for fair evaluation. |
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How to Solve Few-Shot Abusive Content Detection Using the Data We Actually Have (2024.lrec-main)
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| Challenge: | Existing datasets for abusive language detection are expensive and lack of knowledge about the target is a challenge. |
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Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection (2021.findings-acl)
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| Challenge: | Event detection typically does not have sufficient labelled data, thus can be formulated as a few-shot learning problem. |
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HCL-TAT: A Hybrid Contrastive Learning Method for Few-shot Event Detection with Task-Adaptive Threshold (2022.findings-emnlp)
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| Challenge: | Existing methods for event detection have failed to address the problem of constantly emerging event types with limited data. |
| Approach: | They propose a novel method for event detection with a task-adaptive threshold . they propose to learn discriminative representations with 'two-view contrastive loss' |
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Learning Prototype Representations Across Few-Shot Tasks for Event Detection (2021.emnlp-main)
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| Challenge: | Existing training data for event detection are too expensive to achieve in real applications where novel event types emerge . Typical ED systems require labeled data for each predefined event type, but only a few examples are available. |
| Approach: | They propose to introduce cross-task prototypes to model relationships between training tasks in few-shot learning for event detection. |
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