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

## introduction the curse of knowledge @xcite ) is a pervasive cognitive bias exhibited across all domains, leading to discrepancies between an expert's advice and a layman's understanding of it @xcite . take medical consultations as an example: patients often find it difficult to understand their doctors' language. on the other hand, it is important for doctors to accurately disclose the exact illness conditions based on patients' simple vocabulary. misunderstanding may lead to failures in diagnosis and prompt treatment, or even death. how to automatically adjust the expertise level of texts is critical for effective communication. in this paper, we propose a new task of text style transfer between expert language and layman language, namely expertise style transfer, and contribute a manually annotated dataset in the medical many cause dyspnea, pleuritic chest pain, or both. the most common symptoms, regardless of the type of fluid in the pleural space or its cause, are shortness of breath and chest pain. about 1/1000 hypertensive patients has a pheochromocytoma. the incidence of pheochromocytomas may be quite small. the lesion slowly enlarges, often ulcerates, and spread to other skin areas. lesions heal slowly, with scarring. the sores slowly enlarge and spread to nearby tissue, causing further damage. sores heal slowly and may result in permanent scarring. in patients with papilledema, vision is usually not affected initially, but seconds-long graying out of vision, flickering, or blurred or double vision may occur. at first, papilledema may be present without affecting vision. fleeting vision changes (blurred vision, double vision, flickering, or complete loss of vision) typically lasting seconds are characteristic of papilledema. domain for this task. we show four examples in figure 1 , where the upper sentence is for professionals and the lower one is for laymen. on one hand, expertise style transfer aims at improving the readability of a text by reducing the expertise level, such as explaining the complex terminology dyspnea in the first example with a simple phrase shortness of breath. on the other hand, it also aims to improve the expertise level based on context, so that laymen's expressions can be more accurate and professional. for example, in the second pair, causing further damage is not as accurate as ulcerates, omitting the important mucous and disintegrative conditions of the sores. there are two related tasks, but neither serve as suitable prior art. the first is text style transfer (st), which generates texts with different attributes but with the same content. however, although existing approaches have achieved a great success regarding the attributes of sentiment @xcite and formality @xcite among oth-ers, expertise "styling" has not been explored yet. another similar task is text simplification (ts), which rewrites a complex sentence with simple structures @xcite while constrained by limited vocabulary @xcite . this task can be regarded as similar to our subtask: reducing the expertise level from expert to layman language without considering the opposing direction. however, most existing ts datasets are derived from wikipedia, and contain numerous noise (misaligned instances) and inadequacies (instances having non-simplified targets) @xcite ; in which further detailed discussion can be found in section 3.2. in this paper, we construct a manually-annotated dataset for expertise style transfer in medical domain, named msd, and conduct deep analysis by implementing state-of-the-art (sota) ts and st models. the dataset is derived from human-written medical references, the merck manuals foot_0 , which include two parallel versions of texts, one tailored for consumers and the other for healthcare professionals. for automatic evaluation, we hire doctors to annotate the parallel sentences between the two versions (examples shown in figure 1 ). compared with both st and ts datasets, msd is more challenging from two aspects: knowledge gap. domain knowledge is the key factor that influences the expertise level of text, which is also a key difference from conventional styles. we identify two major types of knowledge gaps in msd: terminology, e.g., dyspnea in the first example; and empirical evidence. as shown in the third pair, doctors prefer to use statistics (about 1/1000), while laymen do not (quite small). lexical & structural modification. @xcite has indicated that most st models only perform lexical modification, while leaving structures unchanged. actually, syntactic structures play a significant role in language styles, especially regarding complexity or simplicity @xcite . as shown in the last example, a complex sentence can be expressed with several simple sentences by appropriately splitting content. however, available datasets rarely contain such cases. our main contributions can be summarized as: ## dataset design we describe our dataset construction that comprises three steps: data preprocessing, expert annotation and knowledge incorporation. we then give a detailed analysis. ## experiments we reimplement five sota models from prior ts and st studies on both msd and simpwiki datasets. a further ablation study gives a detailed analysis of the knowledge and structure impacts, and highlights the challenges of existing metrics. ## conclusion we proposed a practical task of expertise style transfer and constructed a high-quality dataset, msd. it is of high quality and also challenging due to the presence of knowledge gap and the need of structural modifications. we established benchmark performance of five sota models. the results shown a significant gap between machine and human performance. our further discussion analyzed the challenges of existing metrics. in the future, we are interested in injecting knowledge into text representation learning @xcite for deeply understanding expert language, and will help to generate knowledgeenhanced questions @xcite for laymen.