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About Deep Biaffine Attention For Neural Dependency Parsing
The document summarizes a research paper that proposes a neural dependency parser using deep biaffine attention. The parser achieves state-of-the-art or near state-of-the-art performance on standard treebanks for six languages. It gets 95.7% unlabeled attachment score and 94.1% labeled attachment score on the English Penn Treebank, outperforming previous graph-based parsers and achieving performance comparable to the best transition-based parser. The proposed parser maintains the simplicity of graph-based approaches while approaching the performance of the best transition-based parser through the use of biaffine attention and other modifications.
Detailed Information
| Author: | ['Thomas Mao'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 8 |
| Language: | English |
| Format: | |
| Price: | FREE |
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