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CLEVR-Math: A Dataset for Compositional Language, Visual and Mathematical Reasoning

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arxiv 2208.05358 v1 pith:YTHAN3Y7 submitted 2022-08-10 cs.LG cs.CLcs.CV

Adam Dahlgren Lindström , Savitha Sam Abraham This is my paper
classification cs.LGcs.CLcs.CV
keywords wordactionsclevr-mathimageproblemsscenevisualdataset
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce CLEVR-Math, a multi-modal math word problems dataset consisting of simple math word problems involving addition/subtraction, represented partly by a textual description and partly by an image illustrating the scenario. The text describes actions performed on the scene that is depicted in the image. Since the question posed may not be about the scene in the image, but about the state of the scene before or after the actions are applied, the solver envision or imagine the state changes due to these actions. Solving these word problems requires a combination of language, visual and mathematical reasoning. We apply state-of-the-art neural and neuro-symbolic models for visual question answering on CLEVR-Math and empirically evaluate their performances. Our results show how neither method generalise to chains of operations. We discuss the limitations of the two in addressing the task of multi-modal word problem solving.

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Cited by 11 Pith papers

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