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Drs Parsing On Pmb 2 2 0
Metrics
F1
Results
Performance results of various models on this benchmark
| Paper Title | Repository | ||
|---|---|---|---|
| Bi-LSTM seq2seq: BERT + characters in 1 encoder | 88.3 | Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT | |
| Transformer seq2seq | 87.1 | Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model | - |
| Character-level bi-LSTM seq2seq + linguistic features | 86.8 | Linguistic Information in Neural Semantic Parsing with Multiple Encoders | - |
| Character-level bi-LSTM seq2seq | 83.3 | Exploring Neural Methods for Parsing Discourse Representation Structures | |
| Neural graph-based system using DAG-grammars | 76.4 | Semantic Graph Parsing with Recurrent Neural Network DAG Grammars | - |
| Transition-based Stack-LSTM | 74.4 | Transition-based DRS Parsing Using Stack-LSTMs | - |
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