Papers by Lyu Zhengyu
HTML: Hierarchical Topology Multi-task Learning for Semantic Parsing in Knowledge Base Question Answering (2025.findings-acl)
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| Challenge: | Existing approaches struggle with mapping questions to precise logical forms . Existing frameworks struggle with complex mapping of questions to logical form . |
| Approach: | They propose a framework that leverages a hierarchical multi-task learning paradigm to enhance the performance of logical form generation. |
| Outcome: | The proposed framework outperforms supervised fine-tuning methods and training-free ones on large language models. |
RTE-GMoE: A Model-agnostic Approach for Relation Triplet Extraction via Graph-based Mixture-of-Expert Mutual Learning (2025.emnlp-main)
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| Challenge: | Relation Triplet Extraction (RTE) is a fundamental while challenge task in knowledge acquisition. |
| Approach: | They propose a mutual learning framework for Relation Triplet Extraction to address this limitation. |
| Outcome: | The proposed framework improves on four state-of-the-art backbones and benchmarks. |