Papers by Yang Zerui
Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and Resources (2021.findings-acl)
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| Challenge: | MRC is a popular task in NLP, aiming to understand a passage and answer the relevant questions. |
| Approach: | They propose a multi-target machine learning task for the medical domain that predicts answers to medical questions and corresponding support sentences from medical information sources simultaneously. |
| Outcome: | The proposed model outperforms baselines by fusing context-aware and knowledge-awful token representations. |
Memo-SQL: Structured Decomposition and Experience-Driven Self-Correction for Training-Free NL2SQL (2026.findings-acl)
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| Challenge: | Existing NL2SQL systems rely on in-context learning with only correct examples . current test-time scaling methods often decompose questions arbitrarily, resulting in poor performance . |
| Approach: | They propose a structured decomposition and experience-aware self-correction framework for NL2SQL . they build a dynamic memory of successful queries and historical error–fix pairs . |
| Outcome: | The proposed framework achieves 68.5% execution accuracy on BIRD, setting new state of the art among open, zero-fine-tuning methods. |
SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining (2021.acl-long)
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| Challenge: | Existing knowledge-based PLMs are based on linked-entity information, but they only use linked-enemy information as auxiliary information. |
| Approach: | They propose to integrate semantic knowledge from neighbours of linked-entity into a medical PLM that integrates heterogeneous-entities into the homogeneously neighbouring entity structure. |
| Outcome: | Experiments show that SMedBERT outperforms baselines in knowledge-intensive Chinese medical tasks. |