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    <abstract>Execution failures are common in daily life when individuals perform procedural tasks, such as cooking or handicrafts making. Retrieving relevant procedural documents that align closely with both the content of steps and the overall execution sequence can help correct these failures with fewer modifications. However, existing retrieval methods, which primarily focus on declarative knowledge, often neglect the execution sequence structures inherent in procedural documents. To tackle this challenge, we introduce a new dataset Procedural Questions, and propose a retrieval model Graph-Fusion Procedural Document Retriever (GFPDR) which integrates procedural graphs with document representations. Extensive experiments demonstrate the effectiveness of GFPDR, highlighting its superior performance in procedural document retrieval compared to existing models.</abstract>
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%0 Conference Proceedings
%T Sequence Structure Aware Retriever for Procedural Document Retrieval: A New Dataset and Baseline
%A Ye, Zhenqi
%A Ren, HaoPeng
%A Cai, Yi
%A Huang, Qingbao
%A Qin, Jing
%A Zhu, Pinli
%A Gong, Songwen
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F ye-etal-2025-sequence
%X Execution failures are common in daily life when individuals perform procedural tasks, such as cooking or handicrafts making. Retrieving relevant procedural documents that align closely with both the content of steps and the overall execution sequence can help correct these failures with fewer modifications. However, existing retrieval methods, which primarily focus on declarative knowledge, often neglect the execution sequence structures inherent in procedural documents. To tackle this challenge, we introduce a new dataset Procedural Questions, and propose a retrieval model Graph-Fusion Procedural Document Retriever (GFPDR) which integrates procedural graphs with document representations. Extensive experiments demonstrate the effectiveness of GFPDR, highlighting its superior performance in procedural document retrieval compared to existing models.
%R 10.18653/v1/2025.findings-emnlp.223
%U https://aclanthology.org/2025.findings-emnlp.223/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.223
%P 4180-4198

Markdown (Informal)

[Sequence Structure Aware Retriever for Procedural Document Retrieval: A New Dataset and Baseline](https://aclanthology.org/2025.findings-emnlp.223/) (Ye et al., Findings 2025)

• Sequence Structure Aware Retriever for Procedural Document Retrieval: A New Dataset and Baseline (Ye et al., Findings 2025)

ACL

• Zhenqi Ye, HaoPeng Ren, Yi Cai, Qingbao Huang, Jing Qin, Pinli Zhu, and Songwen Gong. 2025. Sequence Structure Aware Retriever for Procedural Document Retrieval: A New Dataset and Baseline. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 4180–4198, Suzhou, China. Association for Computational Linguistics.