Papers with absolute F1 points
Variational Graph Autoencoding as Cheap Supervision for AMR Coreference Resolution (2022.acl-long)
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| Challenge: | Abstract Meaning Representation (AMR) is a way to preserve the semantic meaning of a sentence in a graph. |
| Approach: | They propose a general pretraining method that leverages any general AMR corpus and even automatically parses AMR data to achieve performance gains of up to 6% absolute F1 points. |
| Outcome: | The proposed model significantly improves on the previous state-of-the-art model by up to 11% F1. |
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning (2022.acl-long)
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| Challenge: | Existing methods for Named Entity Recognition only learn class-specific semantic features and intermediate representations from source domains, resulting in suboptimal performance. |
| Approach: | They propose a contrastive learning technique that optimizes the inter-token distribution distance for Few-Shot NER. |
| Outcome: | The proposed technique outperforms existing methods by 3%-13% absolute F1 points while showing consistent performance trends. |