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    <abstract>We present a new problem: grounding natural language instructions to mobile user interface actions, and create three new datasets for it. For full task evaluation, we create PixelHelp, a corpus that pairs English instructions with actions performed by people on a mobile UI emulator. To scale training, we decouple the language and action data by (a) annotating action phrase spans in How-To instructions and (b) synthesizing grounded descriptions of actions for mobile user interfaces. We use a Transformer to extract action phrase tuples from long-range natural language instructions. A grounding Transformer then contextually represents UI objects using both their content and screen position and connects them to object descriptions. Given a starting screen and instruction, our model achieves 70.59% accuracy on predicting complete ground-truth action sequences in PixelHelp.</abstract>
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%0 Conference Proceedings
%T Mapping Natural Language Instructions to Mobile UI Action Sequences
%A Li, Yang
%A He, Jiacong
%A Zhou, Xin
%A Zhang, Yuan
%A Baldridge, Jason
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F li-etal-2020-mapping
%X We present a new problem: grounding natural language instructions to mobile user interface actions, and create three new datasets for it. For full task evaluation, we create PixelHelp, a corpus that pairs English instructions with actions performed by people on a mobile UI emulator. To scale training, we decouple the language and action data by (a) annotating action phrase spans in How-To instructions and (b) synthesizing grounded descriptions of actions for mobile user interfaces. We use a Transformer to extract action phrase tuples from long-range natural language instructions. A grounding Transformer then contextually represents UI objects using both their content and screen position and connects them to object descriptions. Given a starting screen and instruction, our model achieves 70.59% accuracy on predicting complete ground-truth action sequences in PixelHelp.
%R 10.18653/v1/2020.acl-main.729
%U https://aclanthology.org/2020.acl-main.729
%U https://doi.org/10.18653/v1/2020.acl-main.729
%P 8198-8210

Markdown (Informal)

[Mapping Natural Language Instructions to Mobile UI Action Sequences](https://aclanthology.org/2020.acl-main.729) (Li et al., ACL 2020)

• Mapping Natural Language Instructions to Mobile UI Action Sequences (Li et al., ACL 2020)

ACL

• Yang Li, Jiacong He, Xin Zhou, Yuan Zhang, and Jason Baldridge. 2020. Mapping Natural Language Instructions to Mobile UI Action Sequences. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8198–8210, Online. Association for Computational Linguistics.