Parallel sentence retrieval from comparable corpora for biomedical text simplification

International audienceParallel sentences provide semantically similar information which can vary on a given dimension , such as language or register. Parallel sentences with register variation (like expert and non-expert documents) can be exploited for the automatic text simplification. The aim of automatic text simplification is to better access and understand a given information. In the biomedical field, simplification may permit patients to understand medical and health texts. Yet, there is currently no such available resources. We propose to exploit comparable corpora which are distinguished by their registers (specialized and simplified versions) to detect and align parallel sentences. These corpora are in French and are related to the biomedical area. Manually created reference data show 0.76 inter-annotator agreement. Our purpose is to state whether a given pair of specialized and simplified sentences is parallel and can be aligned or not. We treat this task as binary classification (alignment/non-alignment). We perform experiments with a controlled ratio of imbalance and on the highly unbalanced real data. Our results show that the method we present here can be used to automatically generate a corpus of parallel sentences from our comparable corpus

Computational Approaches to Assisting Patients\u27 Medical Comprehension from Electronic Health Records

Patient-centered care has been established as a fundamental approach to improve the quality of health care in a seminal report by the Institute of Medicine published at the start of the century. Improved access to health information and demand for greater transparency contributed to its move into the mainstream. Research has also demonstrated that actively involving patients in the management of their own health can lead to better outcomes, and potentially lower costs. However, despite the efforts in many areas of medicine to embrace patient-centered care, engaging patients is still considered a challenge. One of the barriers is the lack of effective tools to help patients understand their health conditions, options and their consequences. Patient portals are now widely adopted by hospitals and other healthcare practices to provide patients with the capabilities to view their own Electronic Health Records. They are a rich resource of information for patients. However, the language in the records are generally difficult for patients without training in medicine to understand. Furthermore, the amount of information can often be overwhelming as well. In this work, we propose computational approaches to foster patient engagement from three aspects by exploiting the rich information in the medical records. First, we design a framework to automatically generate health literacy instruments to measure a patient\u27s literacy levels. This framework exploits readily available large scale corpora to generate instruments in a commonly used test format. Second, we investigate methods that can determine the readability of complex documents such as health records. We propose to rank document readability, instead of assigning a grade level or a pre-defined difficulty category. Lastly, we examine the problem of finding targeted educational materials to facilitate patient comprehension of medical notes. We study methods to formulate effective queries from specialized and long clinical narratives. In addition, we propose a neural network based method to identify medical concepts that are important to patients. The three aspects of this work address the issues of the overabundance and technical complexity of medical language in health records. We demonstrate that our approaches are effective with various experiments and evaluation metric

A text style transfer system for reducing the physician–patient expertise gap:An analysis with automatic and human evaluations

Physicians and patients often come from different backgrounds and have varying levels of education, which can result in communication difficulties in the healthcare process. To address this expertise gap, we present a “Text Style Transfer” system. Our system uses Semantic Textual Similarity techniques based on Sentence Transformers models to create pseudo-parallel datasets from a large, non-parallel corpus of lay and expert texts. This approach allowed us to train a denoising autoencoder model (BART), overcoming the limitations of previous systems. Our extensive analysis, which includes both automatic metrics and human evaluations from both lay (patients) and expert (physicians) individuals, shows that our system outperforms state-of-the-art models and is comparable to human-provided gold references in some cases.</p

A text style transfer system for reducing the physician–patient expertise gap:An analysis with automatic and human evaluations

Physicians and patients often come from different backgrounds and have varying levels of education, which can result in communication difficulties in the healthcare process. To address this expertise gap, we present a “Text Style Transfer” system. Our system uses Semantic Textual Similarity techniques based on Sentence Transformers models to create pseudo-parallel datasets from a large, non-parallel corpus of lay and expert texts. This approach allowed us to train a denoising autoencoder model (BART), overcoming the limitations of previous systems. Our extensive analysis, which includes both automatic metrics and human evaluations from both lay (patients) and expert (physicians) individuals, shows that our system outperforms state-of-the-art models and is comparable to human-provided gold references in some cases.</p

Deep Learning for Text Style Transfer: A Survey

Text style transfer is an important task in natural language generation, which aims to control certain attributes in the generated text, such as politeness, emotion, humor, and many others. It has a long history in the field of natural language processing, and recently has re-gained significant attention thanks to the promising performance brought by deep neural models. In this paper, we present a systematic survey of the research on neural text style transfer, spanning over 100 representative articles since the first neural text style transfer work in 2017. We discuss the task formulation, existing datasets and subtasks, evaluation, as well as the rich methodologies in the presence of parallel and non-parallel data. We also provide discussions on a variety of important topics regarding the future development of this task. Our curated paper list is at https://github.com/zhijing-jin/Text_Style_Transfer_SurveyComment: Computational Linguistics Journal 202