SRA: Semantic Relation-Aware Flowchart Question Answering
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866918338317254656 |
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| author | Li, Xinyu Zou, Bowei Chen, Yuchong Fan, Yifan Hong, Yu |
| author_facet | Li, Xinyu Zou, Bowei Chen, Yuchong Fan, Yifan Hong, Yu |
| contents | Flowchart Question Answering (FlowchartQA) is a multi-modal task that automatically answers questions conditioned on graphic flowcharts. Current studies convert flowcharts into interlanguages (e.g., Graphviz) for Question Answering (QA), which effectively bridge modal gaps between questions and flowcharts. More importantly, they reveal the link relations between nodes in the flowchart, facilitating a shallow relation reasoning during tracing answers. However, the existing interlanguages still lose sight of intricate semantic/logic relationships such as Conditional and Causal relations. This hinders the deep reasoning for complex questions. To address the issue, we propose a novel Semantic Relation-Aware (SRA) FlowchartQA approach. It leverages Large Language Model (LLM) to detect the discourse semantic relations between nodes, by which a link-based interlanguage is upgraded to the semantic relation based interlanguage. In addition, we conduct an interlanguage-controllable reasoning process. In this process, the question intention is analyzed with the aim to determine the depth of reasoning (Shallow or Deep reasoning), as well as the well-matched interlanguage. We experiment on the benchmark dataset FlowVQA. The test results show that SRA yields widespread improvements when upgrading different interlanguages like Graphviz, Mermaid and Plantuml |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_13771 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SRA: Semantic Relation-Aware Flowchart Question Answering Li, Xinyu Zou, Bowei Chen, Yuchong Fan, Yifan Hong, Yu Multimedia Flowchart Question Answering (FlowchartQA) is a multi-modal task that automatically answers questions conditioned on graphic flowcharts. Current studies convert flowcharts into interlanguages (e.g., Graphviz) for Question Answering (QA), which effectively bridge modal gaps between questions and flowcharts. More importantly, they reveal the link relations between nodes in the flowchart, facilitating a shallow relation reasoning during tracing answers. However, the existing interlanguages still lose sight of intricate semantic/logic relationships such as Conditional and Causal relations. This hinders the deep reasoning for complex questions. To address the issue, we propose a novel Semantic Relation-Aware (SRA) FlowchartQA approach. It leverages Large Language Model (LLM) to detect the discourse semantic relations between nodes, by which a link-based interlanguage is upgraded to the semantic relation based interlanguage. In addition, we conduct an interlanguage-controllable reasoning process. In this process, the question intention is analyzed with the aim to determine the depth of reasoning (Shallow or Deep reasoning), as well as the well-matched interlanguage. We experiment on the benchmark dataset FlowVQA. The test results show that SRA yields widespread improvements when upgrading different interlanguages like Graphviz, Mermaid and Plantuml |
| title | SRA: Semantic Relation-Aware Flowchart Question Answering |
| topic | Multimedia |
| url | https://arxiv.org/abs/2602.13771 |