Papers by Shuting Li
MMUIE: Massive Multi-Domain Universal Information Extraction for Long Documents (2026.findings-eacl)
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| Challenge: | Existing document-level information extraction systems operate at the sentence level or within narrow domains due to annotation constraints. |
| Approach: | They propose a large-scale universal dataset for multi-domain, document-level information extraction from long texts. |
| Outcome: | The proposed dataset integrates traditional knowledge bases with large language models to extract fine-grained entities, aliases, and relation triples across 34 domains. |
MRC Examples Answerable by BERT without a Question Are Less Effective in MRC Model Training (2020.aacl-srw)
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| Challenge: | Existing models for Machine Reading Comprehension (MRC) are unable to predict answers from a question and its related context. |
| Approach: | They propose a method that splits the training examples into those that are “easy to answer” or “hard to answer”. |
| Outcome: | The proposed model outperforms the previous models on a large-scale English MRC dataset. |