Papers with absolute F1-score
Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction (2024.findings-eacl)
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| Challenge: | Existing frameworks for fine-grained few-shot entity extraction are difficult to implement in the chemical domain due to the information overload of scientific papers. |
| Approach: | They propose a sequence-to-sequence based few-shot entity extraction approach . it uses a seq2seq entity extractor and a self-validation module to reconstruct original input sentence . |
| Outcome: | The proposed framework achieves 8.26% and 6.84% performance gains on two datasets. |
Bag of Experts Architectures for Model Reuse in Conversational Language Understanding (N18-3)
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| Challenge: | Slot tagging is a key component of natural language understanding systems for personal digital assistants. |
| Approach: | They propose to use a bag of experts architecture to reuse domain data for slot tagging models. |
| Outcome: | Experiments with 10 domains show that the proposed models outperform baseline models by 5.06% and 12.16% when training with only 25% of the training data. |
Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction (D18-1)
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| Challenge: | Existing methods to extract genre-specific and genre-agnostic features require great human effort. |
| Approach: | They propose to use two encoders to explicitly extract genre-specific and genre-agnostic features. |
| Outcome: | The proposed approach outperforms the state-of-the-art by 1.7% on three distinct genres. |