@inproceedings{cao-etal-2020-expertise,
title = "Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen",
author = "Cao, Yixin and
Shui, Ruihao and
Pan, Liangming and
Kan, Min-Yen and
Liu, Zhiyuan and
Chua, Tat-Seng",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.100",
doi = "10.18653/v1/2020.acl-main.100",
pages = "1061--1071",
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 \url{https://srhthu.github.io/expertise-style-transfer/}.",
}
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<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/.</abstract>
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%0 Conference Proceedings
%T Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen
%A Cao, Yixin
%A Shui, Ruihao
%A Pan, Liangming
%A Kan, Min-Yen
%A Liu, Zhiyuan
%A Chua, Tat-Seng
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F cao-etal-2020-expertise
%X 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/.
%R 10.18653/v1/2020.acl-main.100
%U https://aclanthology.org/2020.acl-main.100
%U https://doi.org/10.18653/v1/2020.acl-main.100
%P 1061-1071
Markdown (Informal)
[Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen](https://aclanthology.org/2020.acl-main.100) (Cao et al., ACL 2020)
ACL