An Empirical Investigation of Structured Output Modeling for Graph-based Neural Dependency Parsing (P19-1)
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| Challenge: | In the past few years, graph-based dependency parsers have led to impressive empirical successes on parsing accuracy. |
| Approach: | They propose to use a graph-based dependency parser to model global outputs. |
| Outcome: | The proposed model has been shown to perform better on sentence-level Complete Match metric compared with the previous model. |
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| Challenge: | In graph-based dependency parsers, learning representations is gaining in importance, and we use graph neural networks to learn the representations. |
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| Challenge: | In recent years, dependency parsing has shifted from discrete features to neural networks and continuous representations. |
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| Challenge: | Existing semantic parsers that map sentences to graph-based meaning representations are hand-designed for specific graphbanks. |
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Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training (2020.aacl-main)
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A Root of a Problem: Optimizing Single-Root Dependency Parsing (2021.emnlp-main)
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| Challenge: | Graph-based dependency parsers can be improved without compromising on accuracy or accuracy. |
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