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The Impact of Multiple Parallel Phrase Suggestions on Email Input and Composition Behaviour of Native and Non-Native English Writers

Title data

Buschek, Daniel ; Zürn, Martin ; Eiband, Malin:
The Impact of Multiple Parallel Phrase Suggestions on Email Input and Composition Behaviour of Native and Non-Native English Writers.
2021
Event: CHI Conference on Human Factors in Computing Systems , 08.05.2021 - 13.05.2021 , online (originally: Yokohama, Japan).
(Conference item: Conference , Paper )
DOI: https://doi.org/10.1145/3411764.3445372

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AI Tools - Continuous Interaction with Computational Intelligence ToolsNo information

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https://osf.io/7q4c8/

Abstract in another language

We present an in-depth analysis of the impact of multi-word suggestion choices from a neural language model on user behaviour regarding input and text composition in email writing. Our study for the first time compares different numbers of parallel suggestions, and use by native and non-native English writers, to explore a trade-off of "efficiency vs ideation", emerging from recent literature. We built a text editor prototype with a neural language model (GPT-2), refined in a prestudy with 30 people. In an online study (N=156), people composed emails in four conditions (0/1/3/6 parallel suggestions). Our results reveal (1) benefits for ideation, and costs for efficiency, when suggesting multiple phrases; (2) that non-native speakers benefit more from more suggestions; and (3) further insights into behaviour patterns. We discuss implications for research, the design of interactive suggestion systems, and the vision of supporting writers with AI instead of replacing them.

Further data

Item Type: Conference item (Paper)
Refereed: Yes
Keywords: Text entry; typing; language model; text suggestions; deep learning; neural network; dataset
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Date Deposited: 06 Apr 2021 12:02
Last Modified: 06 Apr 2021 12:02
URI: https://eref.uni-bayreuth.de/id/eprint/64579