One More Modality: Does Abstract Meaning Representation Benefit Visual Question Answering? (2025.findings-emnlp)
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| Challenge: | incorporating explicit semantic information, in the form of Abstract Meaning Representation graphs, can enhance VQA models. |
| Approach: | They augment two vision-language models with sentence- and document-level AMRs . they find that in well-resourced settings, models are negatively impacted by AMR . |
| Outcome: | The proposed model improves in well-resourced and low-resource settings with AMR graphs . the model achieves 13.1% relative gain using sentence-level AMRs compared with the smaller model . |
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Zhijing Jin, Yuen Chen, Fernando Gonzalez Adauto, Jiarui Liu, Jiayi Zhang, Julian Michael, Bernhard Schölkopf, Mona Diab
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| Challenge: | Visual question answering (VQA) is a task that requires an understanding of both the image and the question to provide a natural language answer. |
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| Challenge: | Current Visual Question Answering (VQA) models are trained on labelled data that may be insufficient to learn complex knowledge representations. |
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