Papers with FIDO
Robust Frame-Semantic Models with Lexical Unit Trees and Negative Samples (2024.acl-long)
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| Challenge: | Using a RoBERTa-based filter, we achieve an F1 score of 0.775, surpassing the previous state-of-the-art solution by +0.012. |
| Approach: | They propose a new prefix tree modification to enable robust support for multi-word lexical units and a RoBERTa-based filter to achieve an F1 score of 0.775. |
| Outcome: | The proposed model achieves an F1 score of 0.775, surpassing the state-of-the-art model by +0.012. |