Papers by Zhilin Lei
Simple and Effective Graph-to-Graph Annotation Conversion (2022.coling-1)
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| Challenge: | Existing work on graph-structured annotation conversions has focused on feature-based models which are not easily applicable to new conversions. |
| Approach: | They propose two graph-to-graph conversion approaches which use pseudo data and inherit parameters to guide conversions respectively. |
| Outcome: | The proposed approaches outperform strong baselines with higher conversion score on a graph-structured dataset and other datasets. |
A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples (2021.findings-acl)
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| Challenge: | Neural network-based models have been successful in a wide range of NLP tasks, but their performance is undermined by adversarial examples that would pose no confusion for humans. |
| Approach: | They propose a method to generate high-quality adversarial examples with a higher number of candidate generators and stricter filters and then verify their quality using automatic and human evaluations. |
| Outcome: | The proposed method improves the robustness of English parsing models by relying on adversarial training and model ensembling. |