Interactive Mobile App Navigation with Uncertain or Under-specified Natural Language Commands

• URL: http://arxiv.org/abs/2202.02312v1
• Date: Fri, 4 Feb 2022 18:51:50 GMT
• Title: Interactive Mobile App Navigation with Uncertain or Under-specified Natural Language Commands
• Authors: Andrea Burns, Deniz Arsan, Sanjna Agrawal, Ranjitha Kumar, Kate Saenko, Bryan A. Plummer
• Abstract summary: We introduce Mobile app Tasks with Iterative Feedback (MoTIF), a new dataset where the goal is to complete a natural language query in a mobile app. Current datasets for related tasks in interactive question answering, visual common sense reasoning, and question-answer plausibility prediction do not support research in resolving ambiguous natural language requests. MoTIF contains natural language requests that are not satisfiable, the first such work to investigate this issue for interactive vision-language tasks.
• Score: 47.282510186109775
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Mobile app Tasks with Iterative Feedback (MoTIF), a new dataset where the goal is to complete a natural language query in a mobile app. Current datasets for related tasks in interactive question answering, visual common sense reasoning, and question-answer plausibility prediction do not support research in resolving ambiguous natural language requests or operating in diverse digital domains. As a result, they fail to capture complexities of real question answering or interactive tasks. In contrast, MoTIF contains natural language requests that are not satisfiable, the first such work to investigate this issue for interactive vision-language tasks. MoTIF also contains follow up questions for ambiguous queries to enable research on task uncertainty resolution. We introduce task feasibility prediction and propose an initial model which obtains an F1 score of 61.1. We next benchmark task automation with our dataset and find adaptations of prior work perform poorly due to our realistic language requests, obtaining an accuracy of only 20.2% when mapping commands to grounded actions. We analyze performance and gain insight for future work that may bridge the gap between current model ability and what is needed for successful use in application.

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