Language as Design: Adapting Language to Different Online Audiences
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August, Tal
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Abstract
One of our most powerful language capabilities is our ability to adapt written and spoken language use to different audiences (sometimes referred to as audience design or linguistic accommodation). How we explain a topic to a fifth grader differs from how we explain it to a college student, or how we write about it in a paper. Targeting language messages to different receivers enriches and empowers our communication. However, as online audiences expand in size and demographics, it becomes increasingly difficult to adapt to all potential receivers. Though research papers, news articles, legal documents and social media posts proliferate on the internet, much of their language appeals to an ever-narrowing audience segment. New techniques in natural language processing (NLP) have the potential to make such language adaptation automatic. However, developing systems that effectively rewrite language require an understanding of what language is important to change. In this thesis, we show that language style changes, similar to other interface design changes, influence user behavior and introduce automated systems that design language for different people. We begin by focusing the study of language style changes to the subreddit r/science and show how language in it is associated with changes in people's behavior, potentially restricting access to scientific information. To understand what language is important to change when adapting to different people, we investigate how experts design scientific language for a general audience. We take inspiration from these expert strategies to build Paper Plain – a reading interface for making medical research papers approachable to a general audience. To adjust language to finer-grained audiences, we investigate how people respond to levels of language complexity based on their background knowledge and develop a novel controllable generation method to adjust the complexity of generated summaries. In two user studies we observed that generated summaries using our method leads to similar reader responses as with expert summaries, establishing the feasibility of generating summaries with varying complexities. Our work provides guidance on designing language for specific audiences and adaptable communication at scale. We conclude with a summary of the contributions and a discussion of future research on designing language to encourage better communication online.
Description
Thesis (Ph.D.)--University of Washington, 2022
Keywords
design, human computer interaction, language, natural language processing, science communication, Computer science