Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2020

Abstract

The curse of knowledge can impede communication between experts and laymen. We propose a new task of expertise style transfer and contribute a manually annotated dataset with the goal of alleviating such cognitive biases. Solving this task not only simplifies the professional language, but also improves the accuracy and expertise level of laymen descriptions using simple words. This is a challenging task, unaddressed in previous work, as it requires the models to have expert intelligence in order to modify text with a deep understanding of domain knowledge and structures. We establish the benchmark performance of five state-of-the-art models for style transfer and text simplification. The results demonstrate a significant gap between machine and human performance. We also discuss the challenges of automatic evaluation, to provide insights into future research directions. The dataset is publicly available at https://srhthu.github.io/expertise-style-transfer/.

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Publication

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Virtual Conference, 2020 July 5-10

First Page

1061

Last Page

1071

Identifier

10.18653/v1/2020.acl-main.100

Publisher

Association for Computational Linguistics

City or Country

Virtual Conference

Additional URL

http://doi.org/10.18653/v1/2020.acl-main.100

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