Kaiyuan Gong — accepted AI-conference papers

2025

HTML: Hierarchical Topology Multi-task Learning for Semantic Parsing in Knowledge Base Question Answering

Aziguli Wulamu, Lyu Zhengyu, Kaiyuan Gong, Yu Han, Zewen Wang, Zhihong Zhu +1

ACL 2025finding

Knowledge base question answering (KBQA) aims to answer natural language questions by reasoning over structured knowledge bases. Existing approaches often struggle with the complexity of mapping questions to precise logical forms, particularly when dealing with diverse entities and relations. In thi…

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2025

RTE-GMoE: A Model-agnostic Approach for Relation Triplet Extraction via Graph-based Mixture-of-Expert Mutual Learning

Aziguli Wulamu, Kaiyuan Gong, Lyu Zhengyu, Yu Han, Zhihong Zhu, Bowen Xing

EMNLP 2025

Relation Triplet Extraction (RTE) is a fundamental while challenge task in knowledge acquisition, which identifies and extracts all triplets from unstructured text. Despite the recent advancements, the deep integration of the entity-, relation- and triplet-specific information remains a challenge. I

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