Papers with conditional generation problem
DEGREE: A Data-Efficient Generation-Based Event Extraction Model (2022.naacl-main)
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I-Hung Hsu, Kuan-Hao Huang, Elizabeth Boschee, Scott Miller, Prem Natarajan, Kai-Wei Chang, Nanyun Peng
| Challenge: | Existing models for event extraction require expensive human annotations. |
| Approach: | They propose a data-efficient event extraction model that formulates event extraction as a conditional generation problem. |
| Outcome: | The proposed model can be trained with only a few labeled examples. |
Incorporating Behavioral Hypotheses for Query Generation (2020.emnlp-main)
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| Challenge: | Prior work has focused on extending standard Seq2Seq models but literature often leaves out the influence of clickthrough actions. |
| Approach: | They propose a generic encoder-decoder Transformer framework to generate query suggestions from user inputs. |
| Outcome: | The proposed approach improves top-k word error rate and Bert F1 score compared to a recent BART model. |
DICE: Data-Efficient Clinical Event Extraction with Generative Models (2023.acl-long)
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| Challenge: | EE tasks target specific domains with vague entity boundaries, resulting in a lack of training data. |
| Approach: | They propose a robust and data-efficient generative model for clinical event extraction . they frame event extraction as a conditional generation problem and introduce a contrastive learning objective to decide the boundaries of biomedical mentions. |
| Outcome: | The proposed model is robust and data-efficient for clinical event extraction . it trains an auxiliary mention identification task and event extraction tasks to better identify entity mention boundaries . |