Papers with Universal Proposition Bank
Transferability of Syntax-Aware Graph Neural Networks in Zero-Shot Cross-Lingual Semantic Role Labeling (2024.findings-emnlp)
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| Challenge: | Existing studies in cross-lingual semantic role labeling (SRL) lack a comprehensive analysis of their network selection. |
| Approach: | They compare the transferability of graph neural network-based models with universal dependency trees to English and 23 target languages. |
| Outcome: | The proposed models perform better in resource-poor languages than in resource rich ones. |
High-order Refining for End-to-end Chinese Semantic Role Labeling (2020.aacl-main)
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| Challenge: | Current semantic role labeling methods are limited to short-term features and local decisions. |
| Approach: | They propose a high-order refining mechanism to perform interaction between all predicate-argument pairs via attention calculation. |
| Outcome: | The proposed model achieves state-of-the-art on Chinese SRL data, including CoNLL09 and Universal Proposition Bank, while relieving the long-range dependency issues. |
Alignment-free Cross-lingual Semantic Role Labeling (2020.emnlp-main)
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| Challenge: | Existing approaches to semantic role labeling rely on word alignments, translation engines or preprocessing tools. |
| Approach: | They propose a cross-lingual semantic role labeling model which only requires annotations in a source language and access to raw text in . |
| Outcome: | The proposed model minimizes the effort required to construct annotations or models for a new target language. |
Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus (2020.acl-main)
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| Challenge: | Existing approaches to semantic role labeling (SRL) are focusing on the English language. |
| Approach: | They propose a method for semantic role labeling that uses corpus translation to build training datasets from SRL annotations. |
| Outcome: | The proposed method is highly effective and can improve the target-language performance significantly. |