Papers with self-training-based methods
LogicST: A Logical Self-Training Framework for Document-Level Relation Extraction with Incomplete Annotations (2024.emnlp-main)
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| Challenge: | Document-level relation extraction (DocRE) is difficult due to the vast number of entity pairs. |
| Approach: | They propose a neural-logic self-training framework that iteratively resolves conflicts and constructs the minimal diagnostic set for updating models. |
| Outcome: | The proposed framework outperforms existing methods on the document-level relation extraction (docRE) benchmark. |
Adaptive Unsupervised Self-training for Disfluency Detection (2022.coling-1)
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| Challenge: | Recent studies on disfluency detection heavily relies on human annotations, which are difficult and expensive to obtain in practice. |
| Approach: | They propose an unsupervised method that reweights the importance of each training example according to its grammatical feature and prediction confidence. |
| Outcome: | The proposed method improves 2.3 points over the current SOTA unsupervised method and is competitive with the SOTA supervised method. |