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Mobile App Tasks with Iterative Feedback (MoTIF): Addressing Task Feasibility in Interactive Visual Environments

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arxiv 2104.08560 v1 pith:HMUKGLAN submitted 2021-04-17 cs.CL cs.CV

Mobile App Tasks with Iterative Feedback (MoTIF): Addressing Task Feasibility in Interactive Visual Environments

Andrea Burns , Deniz Arsan , Sanjna Agrawal , Ranjitha Kumar , Kate Saenko , Bryan A. Plummer This is my paper
classification cs.CL cs.CV
keywords environmentsinteractivefeasibilitylanguagemotifnaturaltasktasks
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In recent years, vision-language research has shifted to study tasks which require more complex reasoning, such as interactive question answering, visual common sense reasoning, and question-answer plausibility prediction. However, the datasets used for these problems fail to capture the complexity of real inputs and multimodal environments, such as ambiguous natural language requests and diverse digital domains. We introduce Mobile app Tasks with Iterative Feedback (MoTIF), a dataset with natural language commands for the greatest number of interactive environments to date. MoTIF is the first to contain natural language requests for interactive environments that are not satisfiable, and we obtain follow-up questions on this subset to enable research on task uncertainty resolution. We perform initial feasibility classification experiments and only reach an F1 score of 37.3, verifying the need for richer vision-language representations and improved architectures to reason about task feasibility.

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Cited by 4 Pith papers

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