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Exploring Natural Language Processing Methods for Interactive Behaviour Modelling

Guanhua Zhang, Matteo Bortoletto, Zhiming Hu, Lei Shi, Mihai Bâce, and Andreas Bulling

Proc. IFIP TC13 Conference on Human-Computer Interaction (INTERACT), 2023, York, UK


This repository contains the EMAKI dataset:

  • user: User ID 1~39
  • task: Text entry and editing (3), image editing (4) and questionnaire completion (5)
  • trial: Questionnaire completion had four trials
  • type: Action type, one of ['mousemove', 'mousedown', 'mouseup', 'keydown', 'keyup']
  • timestamp: Timestamp of the current mouse or keyboard action
  • X/Y: On-screen coordinates of the current mouse cursor
  • value: Left (1) or right (3) if type is mousedown/up; keystroke content if type is keydown/up; nan/none otherwise
  • resolutionX/Y: The resolution of user's screen when performing the online user study

As well as code for

  • Byte pair encoding (BPE)
  • The Transformer-based classifier

If you find our work helpful, please cite:

author="Zhang, Guanhua
and Bortoletto, Matteo
and Hu, Zhiming
and Shi, Lei
and B{\^a}ce, Mihai
and Bulling, Andreas",
title="Exploring Natural Language Processing Methods for Interactive Behaviour Modelling",
booktitle="Human-Computer Interaction -- INTERACT 2023",
publisher="Springer Nature Switzerland",