CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies（阿里巴巴-浙江大学前沿技术联合研究院）；Zhejiang University（浙江大学）；Alibaba Group（阿里巴巴集团）；Pingan Health Technology（平安健康科技）；Ping An Health Cloud Company Limited（平安健康云有限公司）；Ping An International Smart City Technology Co., Ltd（平安国际智慧城市科技股份有限公司）；Peking University（北京大学）；Peng Cheng Laboratory（鹏城实验室）；Tongji University（同济大学）；Philips Research China（飞利浦中国研究院）；Tsinghua University（清华大学）
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice. With the development of biomedical language understanding benchmarks, AI applications are widely used in the medical field. However, most benchmarks are limited to English, which makes it challenging to replicate many of the successes in English for other languages. To facilitate research in this direction, we collect real-world biomedical data and present the first Chinese Biomedical Language Understanding Evaluation (CBLUE) benchmark: a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification, and an associated online platform for model evaluation, comparison, and analysis. To establish evaluation on these tasks, we report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling. Our benchmark is released at \url{https://tianchi.aliyun.com/dataset/dataDetail?dataId=95414&lang=en-us}.
Advancing Chinese biomedical text mining with community challenges
Sichuan University（四川大学）；West China Hospital（华西医院）；Tongji University（同济大学）；Shanghai Business School（上海商学院）；Takeda Co. Ltd.（武田制药有限公司）；Harbin Institute of Technology（哈尔滨工业大学）
Shanghai East Hospital（上海东方医院）；Tongji University School of Medicine（同济大学医学院）；Institute of Ethnology and Anthropology（民族学与人类学研究所）；Chinese Academy of Social Sciences（中国社会科学院）
Biomedical text mining is becoming increasingly important as the number of biomedical documents and web data rapidly grows. Recently, word representation models such as BERT has gained popularity among researchers. However, it is difficult to estimate their performance on datasets containing biomedical texts as the word distributions of general and biomedical corpora are quite different. Moreover, the medical domain has long-tail concepts and terminologies that are difficult to be learned via language models. For the Chinese biomedical text, it is more difficult due to its complex structure an…
LingShu: A Large-Scale Symptom-Centric Contextualized Knowledge Graph Bridging Traditional Chinese Medicine and Modern Biomedicine
Beijing Jiaotong University（北京交通大学）；Hubei Provincial Hospital of Traditional Chinese Medicine（湖北省中医院）；Hubei University of Chinese Medicine（湖北中医药大学）；Hubei Province Academy of Traditional Chinese Medicine（湖北省中医药研究院）；China Academy of Chinese Medical Sciences（中国中医科学院）；Xiyuan Hospital（西苑医院）；National Clinical Research Center for Chinese Medicine Cardiology（国家中医心血管病临床医学研究中心）；Hubei Provincial Clinical Research Center for Acupuncture and Moxibustion in Obesity Treatment（湖北省肥胖病针灸临床研究中心）；Hubei Shizhen Laboratory（湖北时珍实验室）；Tianjin Ta（天津（此处原文未完整，按原文提取））
A Unified Biomedical Named Entity Recognition Framework with Large Language Models
College of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；Air Force Communications NCO Academy（空军通信NCO学院）；Department of Pharmacy, Second Affiliated Hospital of Dalian Medical University（大连医科大学第二附属医院药学部）
Recently, with the continuous development of deep learning, the performance of named entity recognition tasks has been dramatically improved. However, the privacy and the confidentiality of data in some specific fields, such as biomedical and military, cause insufficient data to support the training of deep neural networks. In this paper, we propose an encryption learning framework to address the problems of data leakage and inconvenient disclosure of sensitive data in certain domains. We introduce multiple encryption algorithms to encrypt training data in the named entity recognition task for…
Distilling Closed-Source LLM's Knowledge for Locally Stable and Economic Biomedical Entity Linking
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；University of Chinese Academy of Sciences（中国科学院大学）；Changsha Aier Eye Hospital（长沙爱尔眼科医院）；Portland State University（波特兰州立大学）
Baidu Inc.（百度公司）；University of Science and Technology of China（中国科学技术大学）
Pre-trained language models (PLMs), such as BERT and GPT, have revolutionized the field of NLP, not only in the general domain but also in the biomedical domain. Most prior efforts in building biomedical PLMs have resorted simply to domain adaptation and focused mainly on English. In this work we introduce eHealth, a Chinese biomedical PLM built from scratch with a new pre-training framework. This new framework pre-trains eHealth as a discriminator through both token- and sequence-level discrimination. The former is to detect input tokens corrupted by a generator and recover their original ide…
PromptCBLUE: A Chinese Prompt Tuning Benchmark for the Medical Domain
East China Normal University（华东师范大学）；Alibaba Group（阿里巴巴集团）；Holoflow Digital Technology（全息数字技术（杭州）有限公司）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））
Baidu Research（百度研究院）；Oregon State University（俄勒冈州立大学）
Machine translation requires large amounts of parallel text. While such datasets are abundant in domains such as newswire, they are less accessible in the biomedical domain. Chinese and English are two of the most widely spoken languages, yet to our knowledge a parallel corpus in the biomedical domain does not exist for this language pair. In this study, we develop an effective pipeline to acquire and process an English-Chinese parallel corpus, consisting of about 100,000 sentence pairs and 3,000,000 tokens on each side, from the New England Journal of Medicine (NEJM). We show that training on…
This paper introduces WeChat's participation in WMT 2022 shared biomedical translation task on Chinese to English. Our systems are based on the Transformer, and use several different Transformer structures to improve the quality of translation. In our experiments, we employ data filtering, data generation, several variants of Transformer, fine-tuning and model ensemble. Our Chinese$\to$English system, named Summer, achieves the highest BLEU score among all submissions.
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Peking University（北京大学）；Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences（中国科学院脑科学与智能技术卓越创新中心）
Named entity recognition (NER) is a foundational technology for information extraction. This paper presents a flexible NER framework compatible with different languages and domains. Inspired by the idea of distant supervision (DS), this paper enhances the representation by increasing the entity-context diversity without relying on external resources. We choose different layer stacks and sub-network combinations to construct the bilateral networks. This strategy can generally improve model performance on different datasets. We conduct experiments on five languages, such as English, German, Span…
ISPO: An Integrated Ontology of Symptom Phenotypes for Semantic Integration of Traditional Chinese Medical Data
Beijing Jiaotong University（北京交通大学）；Hubei Provincial Hospital of Traditional Chinese Medicine（湖北省中医院）；China Academy of Chinese Medical Sciences（中国中医科学院）；Hubei University of Chinese Medicine（湖北中医药大学）；Guang'anmen Hospital, China Academy of Chinese Medical Sciences（中国中医科学院广安门医院）；Zhejiang University（浙江大学）
KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language Model
Institute of Artificial Intelligence, Soochow University（苏州大学人工智能研究院）；Peking University（北京大学）；The Hong Kong Polytechnic University（香港理工大学）；Pharmcube（药智魔方）；Changping Laboratory（昌平实验室）
Tsinghua University（清华大学）；Chinese Academy of Medical Sciences/Peking Union Medical College（中国医学科学院/北京协和医学院）
Objective: Today's neural machine translation (NMT) can achieve near human-level translation quality and greatly facilitates international communications, but the lack of parallel corpora poses a key problem to the development of translation systems for highly specialized domains, such as biomedicine. This work presents an unsupervised algorithm for deriving parallel corpora from document-level translations by using sentence alignment and explores how training materials affect the performance of biomedical NMT systems. Materials and Methods: Document-level translations are mixed to train bilin…
