Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen

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 stateof-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/.

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“…In the future, we are interested in cross-KGs inference and transfer , and investigating how to inject knowledge into deep learning architectures, such as for information extraction (Tong et al, 2020) or text generation (Cao et al, 2020b). test family relationships including cousin, ancestor, marriage, parent, sibling, and uncle, among the members of 5 families along 6 generations.…”

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“…Wang et al (2019) transfer text style by updating the latent representation (referred to as TAE) based on the Fast Gradient Sign Method (FGSM) . Liu et al (2020) also use a gradient-based optimization to update the latent representation.…”

“…However, learning disentangled representations is often challenging; and multiple attributespecific decoders are commonly required for text generation, which is undesirable especially when transferring multiple attributes. The entangled representations, on the other hand, has been shown to achieve promising performance on the content preservation and to produce fluent sentences with a much less complicated architecture (Lample et al, 2019;Wang et al, 2019;Liu et al, 2020).…”

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