@inproceedings{wulamu-etal-2025-html,
title = "{HTML}: Hierarchical Topology Multi-task Learning for Semantic Parsing in Knowledge Base Question Answering",
author = "Wulamu, Aziguli and
Zhengyu, Lyu and
Gong, Kaiyuan and
Han, Yu and
Wang, Zewen and
Zhu, Zhihong and
Xing, Bowen",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.485/",
doi = "10.18653/v1/2025.findings-acl.485",
pages = "9307--9321",
ISBN = "979-8-89176-256-5",
abstract = "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 this paper, we propose Hierarchical Topology Multi-task Learning (HTML), a novel framework that leverages a hierarchical multi-task learning paradigm to enhance the performance of logical form generation. Our framework consists of a main task: generating logical forms from questions, and three auxiliary tasks: entity prediction from the input question, relation prediction for the given entities, and logical form generation based on the given entities and relations. Through joint instruction-tuning, HTML allows mutual guidance and knowledge transfer among the hierarchical tasks, capturing the subtle dependencies between entities, relations, and logical forms. Extensive experiments on public benchmarks show that HTML markedly outperforms both supervised fine-tuning methods and training-free ones based on powerful large language models (e.g., GPT-4), demonstrating its superiority in question understanding and structural knowledge reasoning."
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<abstract>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 this paper, we propose Hierarchical Topology Multi-task Learning (HTML), a novel framework that leverages a hierarchical multi-task learning paradigm to enhance the performance of logical form generation. Our framework consists of a main task: generating logical forms from questions, and three auxiliary tasks: entity prediction from the input question, relation prediction for the given entities, and logical form generation based on the given entities and relations. Through joint instruction-tuning, HTML allows mutual guidance and knowledge transfer among the hierarchical tasks, capturing the subtle dependencies between entities, relations, and logical forms. Extensive experiments on public benchmarks show that HTML markedly outperforms both supervised fine-tuning methods and training-free ones based on powerful large language models (e.g., GPT-4), demonstrating its superiority in question understanding and structural knowledge reasoning.</abstract>
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%0 Conference Proceedings
%T HTML: Hierarchical Topology Multi-task Learning for Semantic Parsing in Knowledge Base Question Answering
%A Wulamu, Aziguli
%A Zhengyu, Lyu
%A Gong, Kaiyuan
%A Han, Yu
%A Wang, Zewen
%A Zhu, Zhihong
%A Xing, Bowen
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wulamu-etal-2025-html
%X 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 this paper, we propose Hierarchical Topology Multi-task Learning (HTML), a novel framework that leverages a hierarchical multi-task learning paradigm to enhance the performance of logical form generation. Our framework consists of a main task: generating logical forms from questions, and three auxiliary tasks: entity prediction from the input question, relation prediction for the given entities, and logical form generation based on the given entities and relations. Through joint instruction-tuning, HTML allows mutual guidance and knowledge transfer among the hierarchical tasks, capturing the subtle dependencies between entities, relations, and logical forms. Extensive experiments on public benchmarks show that HTML markedly outperforms both supervised fine-tuning methods and training-free ones based on powerful large language models (e.g., GPT-4), demonstrating its superiority in question understanding and structural knowledge reasoning.
%R 10.18653/v1/2025.findings-acl.485
%U https://aclanthology.org/2025.findings-acl.485/
%U https://doi.org/10.18653/v1/2025.findings-acl.485
%P 9307-9321
Markdown (Informal)
[HTML: Hierarchical Topology Multi-task Learning for Semantic Parsing in Knowledge Base Question Answering](https://aclanthology.org/2025.findings-acl.485/) (Wulamu et al., Findings 2025)
ACL