Investigating Non-local Features for Neural Constituency Parsing (2022.acl-long)
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| Challenge: | Constituency parsers have been able to achieve competitive performance by using local features. |
| Approach: | They propose to inject non-local features into the training process of a local span-based parser by predicting constituent n-gram non-local patterns and ensuring consistency between constituents and local constituents. |
| Outcome: | The proposed method outperforms the self-attentive parser in multi-lingual and zero-shot cross-domain settings. |
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| Challenge: | Non-local features have been shown crucial for statistical parsing, but local models can give highly competitive accuracies thanks to the power of dense neural input representations. |
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| Challenge: | Neural parsers perform well on in-domain benchmarks, but their performance degrades in well-understood ways. |
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