Improved Touch-screen Inputting Using Sequence-level Prediction Generation

Xin Wang, Xu Li, Jinxing Yu, Mingming Sun, Ping Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Recent years have witnessed the continuing growth of people's dependence on touchscreen devices. As a result, input speed with the onscreen keyboard has become crucial to communication efficiency and user experience. In this work, we formally discuss the general problem of input expectation prediction with a touch-screen input method editor (IME). Taken input efficiency as the optimization target, we proposed a neural end-to-end candidates generation solution to handle automatic correction, reordering, insertion, deletion as well as completion. Evaluation metrics are also discussed base on real use scenarios. For a more thorough comparison, we also provide a statistical strategy for mapping touch coordinate sequences to text input candidates. The proposed model and baselines are evaluated on a real-world dataset. The experiment (conducted on the PaddlePaddle deep learning platform1) shows that the proposed model outperforms the baselines.

Original languageEnglish (US)
Title of host publicationThe Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020
PublisherAssociation for Computing Machinery, Inc
Pages3077-3083
Number of pages7
ISBN (Electronic)9781450370233
DOIs
StatePublished - Apr 20 2020
Externally publishedYes
Event29th International World Wide Web Conference, WWW 2020 - Taipei, Taiwan, Province of China
Duration: Apr 20 2020Apr 24 2020

Publication series

NameThe Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020

Conference

Conference29th International World Wide Web Conference, WWW 2020
Country/TerritoryTaiwan, Province of China
CityTaipei
Period4/20/204/24/20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

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