Papers with Chinese Penn Treebank
Bidirectional Masked Self-attention and N-gram Span Attention for Constituency Parsing (2023.findings-emnlp)
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| Challenge: | Existing attention mechanisms for constituency parsing lack directional information needed to form sentence spans. |
| Approach: | They propose a bidirectional masked and N-gram span Attention model which captures the explicit dependencies between each word and enhances the representation of the output span vectors. |
| Outcome: | The proposed model achieves state-of-the-art performance on the Penn Treebank and Chinese Penn TreeBank datasets with F1 scores of 96.47 and 94.15 respectively. |
Head-Driven Phrase Structure Grammar Parsing on Penn Treebank (P19-1)
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| Challenge: | Head-driven phrase structure grammars have a uniform formalism representing rich contextual syntactic and even semantic meanings. |
| Approach: | They propose to integrate constituent and dependency formal representations into head-driven phrase structure. |
| Outcome: | The proposed parser achieves state-of-the-art performance on Penn Treebank and Chinese Penn TreeBank. |
An In-depth Study on Internal Structure of Chinese Words (2021.acl-long)
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Chen Gong, Saihao Huang, Houquan Zhou, Zhenghua Li, Min Zhang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan
| Challenge: | Unlike English letters, Chinese characters have rich and specific meanings. |
| Approach: | They propose to model Chinese words' internal structures as dependency trees with 11 labels for distinguishing syntactic relationships. |
| Outcome: | The proposed model of Chinese word-internal structures shows it can be used to parse sentences . it shows that the model can be applied to a sentence-level task with a competitive dependency parser. |