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

TL;DR

This study investigates how multiple parallel phrase suggestions from a neural language model influence email writing, revealing a trade-off between idea generation and efficiency, with non-native speakers benefiting more from increased suggestions.

Contribution

It is the first to compare different numbers of parallel suggestions and their effects on native and non-native English writers in email composition.

Findings

Multiple suggestions enhance ideation but reduce efficiency.

Non-native speakers gain more from additional suggestions.

Insights into user behavior patterns during email writing.

Abstract

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.