Welcome to ROCLING 2022!

Important Dates

  • Paper Submission Due: August 13 27 (Sat), 2022
  • Notification of acceptance: September 30 (Fri), 2022
  • Camera-ready due: October 7 (Fri), 2022
  • Early Registration ends: October 14 (Fri), 2022
  • Late Registration ends: November 4 (Fri), 2022
  • On-Site Registration: November 21 - 22 (Mon - Tue), 2022
  • All deadlines are 11.59 pm UTC-12h (anywhere on earth)

Welcome to ROCLING 2022!

ROCLING 2022 is the 34th annual Conference on Computational Linguistics and Speech Processing in Taiwan sponsored by the Association for Computational Linguistics and Chinese Language Processing (ACLCLP). The conference will be held in NTUH International Convention Center, Taipei city, Taiwan during November 21-22, 2022.

ROCLING 2022 will provide an international forum for researchers and industry practitioners to share their new ideas, original research results and practical development experiences from all language and speech research areas, including computational linguistics, information understanding, and signal processing. ROCLING 2022 will feature oral papers, posters, tutorials, special sessions and shared tasks.

The conference on Computational Linguistics and Speech Processing (ROCLING) was initiated in 1988 by the Association for Computational Linguistics and Chinese Language Processing (ACLCLP) with the major goal to provide a platform for researchers and professionals from around the world to share their experiences related to natural language processing and speech processing. Following are a list of past ROCLING conferences.

Below, all information on the program of ROCLING 2022 are given that are available so far. More details to be announced.

Below, all information on the program of ROCLING 2022 are given that are available so far. More details to be announced.


More details to be announced.

Prof. Makoto P. Kato

Matching Texts with Data for Evidence-based Information Retrieval

Speaker: Prof. Makoto P. Kato

  • Professor, University of Tsukuba, Japan
  • Time: TBC
  • Session Chair: TBC


Makoto P. Kato received his Ph.D. degree in Graduate School of Informatics from Kyoto University, Sakyo Ward, Yoshidahonmachi, in 2012. Currently, he is an associate professor of Faculty of Library, Information and Media Science, University of Tsukuba, Japan. In 2008, he was awarded 'WISE 2008 Kambayashi Best Paper Award' through the article 'Can Social Tagging Improve Web Image Search?' with other researchers. In 2010, he served as a JSPS Research Fellow in Japan Society for the Promotion of Science. During the period 2010 to 2012, he also served in Microsoft Research Asia Internship (under supervision by Dr. Tetsuya Sakai in WIT group), Microsoft Research Asia Internship (under supervision by Dr. Tetsuya Sakai in WSM group), and Microsoft Research Internship (under supervision by Dr. Susan Dumais in CLUES group). From 2012, he worked as an assistant professor in Graduate School of Informatics, Kyoto University, Japan. His research and teaching career began, and he worked as an associate professor from 2019 in Graduate School of Informatics, Kyoto University, Japan. His research interests include Information Retrieval, Web Mining, and Machine Learning, while he is an associate professor in Knowledge Acquisition System Laboratory (Kato Laboratory), University of Tsukuba, Japan.



Prof. Junichi Yamagishi

Title: TBC

Speaker: Prof. Junichi Yamagishi

  • Professor, National Institute of Informatics, Japan
  • Time: TBC
  • Session Chair: TBC


Junichi Yamagishi received the Ph.D. degree from Tokyo Institute of Technology in 2006 for a thesis that pioneered speaker-adaptive speech synthesis. He is currently a Professor with the National Institute of Informatics, Tokyo, Japan, and also a Senior Research Fellow with the Centre for Speech Technology Research, University of Edinburgh, Edinburgh, U.K. Since 2006, he has authored and co-authored more than 250 refereed papers in international journals and conferences. He was an area coordinator at Interspeech 2012. He was one of organizers for special sessions on “Spoofing and Countermeasures for Automatic Speaker Verification” at Interspeech 2013, “ASVspoof evaluation” at Interspeech 2015, “Voice conversion challenge 2016” at Interspeech 2016, “2nd ASVspoof evaluation” at Interspeech 2017, and “Voice conversion challenge 2018” at Speaker Odyssey 2018. He is currently an organizing committee for ASVspoof 2019, an organizing committee for ISCA the 10th ISCA Speech Synthesis Workshop 2019, a technical program committee for IEEE ASRU 2019, and an award committee for ISCA Speaker Odyssey 2020. He was a member of IEEE Speech and Language Technical Committee. He was also an Associate Editor of the IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING and a Lead Guest Editor for the IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING special issue on Spoofing and Countermeasures for Automatic Speaker Verification. He is currently a guest editor for Computer Speech and Language special issue on speaker and language characterization and recognition: voice modeling, conversion, synthesis and ethical aspects. He also serves as a chairperson of ISCA SynSIG currently. He was the recipient of the Tejima Prize as the best Ph.D. thesis of Tokyo Institute of Technology in 2007. He received the Itakura Prize from the Acoustic Society of Japan in 2010, the Kiyasu Special Industrial Achievement Award from the Information Processing Society of Japan in 2013, the Young Scientists’ Prize from the Minister of Education, Science and Technology in 2014, the JSPS Prize from Japan Society for the Promotion of Science in 2016, and Docomo mobile science award from Mobile communication fund in 2018.




More details to be announced.

Early Registration

(Before October 14, 2022)
  • ACLCLP Member: NT$(TBC)
  • ACLCLP Non-Member: NT$(TBC)
  • ACLCLP Member: NT$(TBC)
  • ACLCLP Non-Member: NT$(TBC)
  • Free

Late Registration

(Before November 4, 2022)
  • ACLCLP Member: NT$(TBC)
  • ACLCLP Non-Member: NT$(TBC)
  • ACLCLP Member: NT$(TBC)
  • ACLCLP Non-Member: NT$(TBC))
  • Free

On-Site Registration

(November 21 - 22, 2022)
  • ACLCLP Member: NT$(TBC)
  • ACLCLP Non-Member: NT$(TBC)
  • ACLCLP Member: NT$(TBC))
  • ACLCLP Non-Member: NT$(TBC))
  • Free

附註說明/Registration Fees

  • 每篇會議論文的發表至少要繳交一筆「一般人士」報名費。
  • 報名費一經繳費後恕不接受退費,報名費收據將連同會議資料於 11/14 一併郵寄。
  • ACLCLP Member 為「中華民國計算語言學學會」 之有效會員
  • 本年度尚未繳交年費之舊會員或失效之會員,報名之與會身份/Category請勾選「….(會員+會費)」,勿需再申請入會
  • 非會員欲同時申請入會者,請先至學會網頁之「會員專區」申請加入會員;報名之「與會身份/Category」請勾選「….(會員+會費)」。 (前往會員專區)
  • 以「學生新會員」及「學生非會員」身份報名者,請於報名時上傳學生身份證明。
  • 以「學生新會員」及「學生非會員」身份報名者,請於報名時上傳學生身份證明。
  • 報名完成後,若需更正個人資料,請於 11/11 前以Email方式聯絡大會。

Registration Details

  • One Regular registration can cover a maximum of One Paper. Student registration can NOT cover paper.
  • Registration fees are non-refundable.
  • International registrants have to pay by credit card only (Visa or Master Card). All the conference registration payment will be charged in currency of New Taiwan dollars.
  • For “full-time Students”, please upload the image (or pdf) of student ID card.


  • Early Registration-10/14(Fri)以前:報名費應於 10/21(Fri)前繳交。
  • Late Registration-11/4(Fri):報名費應於11/21(Mon)前繳交。
  • On-Site Registration-11/4(Fri)線上報名截止,擬報名者請於11/18(Fri)線上報名。

Important Dates for Registration

  • Early Registration due by October 14. Payment must be received before October 21.
  • Registration between November 4. Payment must be received before November 11.
  • The registration site will be closed on November 4.

Special Session

More details to be announced.

ROCLING 2022 Shared Task

Chinese Healthcare Named Entity Recognition


李龍豪 Lung-Hao Lee

Department of Electrical Engineering National Central University

陳昭沂 Chao-Yi Chen

Department of Electrical Engineering National Central University

禹良治 Liang-Chih Yu

Department of Information Management Yuan Ze University

曾元顯 Yuen-Hsien Tseng
Graduate Institute of Library and Information Studies National Taiwan Normal University

How to participate? Registration here (Due: August 20, 2022)

I. Background

Named Entity Recognition (NER) is a fundamental task in information extraction that locates the mentions of named entities and classifies them (e.g., person, organization and location) in unstructured texts. The NER task has traditionally been solved as a sequence labeling problem, where entity boundaries and category labels are jointly predicted. Various methods have been proposed to tackle this research problem, including Hidden Markov Models (HMM) (Ponomareva et al., 2007), Maximum Entropy Markov Models (MEMM) (Chieu and Ng, 2003) and Conditional Random Field (CRF) (Wei et al., 2015). Recently, neural networks have been shown to achieve impressive results. The current state-of-the-art for English NER has been achieved by using LSTM (Long Short-Term Memory)- CRF based networks (Chiu and Nichols, 2016; Lample et al., 2016; Ma and Hovy, 2016; Liu et al., 2018).

Chinese NER is more difficult to process than English NER. Chinese language is logographic and provides no conventional features like capitalization. In addition, due to a lack of delimiters between characters, Chinese NER is correlated with word segmentation, and named entity boundaries are also word boundaries. However, incorrectly segmented entity boundaries will cause error propagation in NER. For example, in a particular context, a disease entity “思覺失調症” (schizophrenia) may be incorrectly segmented into three words: “思覺” (thinking and feeling), “失調” (disorder) and “症” (disease). Hence, it has been shown that character-based methods outperform word-based approaches for Chinese NER (He and Wang, 2008; Li et al., 2014; Zhang and Yang, 2018).

In the digital era, healthcare information-seeking users usually search and browse web content in click-through trails to obtain healthcare-related information before making a doctor’s appointment for diagnosis and treatment. Web texts are valuable sources to provide healthcare information such as health-related news, digital health magazines and medical question/answer forums. Domain-specific healthcare information includes many proper names, mainly as named entities. For example, “三酸甘油酯” (triglyceride) is a chemical found in the human body; “電腦斷層掃描” (computed tomography; CT) is medical imaging procedure that uses computer-processed combinations of X-ray measurements to produce tomographic images of specific areas of the human body, and “靜脈免疫球蛋白注射” (intravenous immunoglobulin; IVIG) is a kind of treatment for avoiding infections. In summary, Chinese healthcare NER is an important and essential task in natural language processing to automatically identify healthcare entities such as symptoms, chemicals, diseases, and treatments for machine reading and understanding.

II. Task Description

A total of 10 entity types are described and some examples are provided in Table I for Chinese healthcare named entity recognition. In this task, participants are asked to predict the named entity boundaries and categories for each given sentence. We use the common BIO (Beginning, Inside, and Outside) format for NER tasks. The B-prefix before a tag indicates that the character is the beginning of a named entity and I-prefix before a tag indicates that the character is inside a named entity. An O tag indicates that a token belongs to no named entity. Below are the example sentences.

Example 1:
● Input: 修復肌肉與骨骼最重要的便是熱量、蛋白質與鈣質。
● Output: O, O, B-BODY, I-BODY, O, B-BODY, I-BODY, O, O, O, O, O, O, O, O, O, B-CHEM, I-CHEM, I-CHEM, O, B-CHEM, I-CHEM, O

Example 2:
● Input: 如何治療胃食道逆流症?

Table 1. Named Entity Types with Descriptions and Examples

Entity Type Description Examples
Body (BODY) The whole physical structure that forms a person or animal including biological cells, organizations, organs and systems. “細胞核” (nucleus), “神經組織” (nerve tissue), “左心房” (left atrium), “脊髓” (spinal cord), “呼吸系統” (respiratory system)
Symptom (SYMP) Any feeling of illness or physical or mental change that is caused by a particular disease. “流鼻水” (rhinorrhea), “咳嗽” (cough), “貧血” (anemia), “失眠” (insomnia), “心悸” (palpitation), “耳鳴” (tinnitus)
Instrument (INST) A tool or other device used for performing a particular medical task such as diagnosis and treatments. “血壓計” (blood pressure meter), “達文西手臂” (DaVinci Robots), “體脂肪計” (body fat monitor), “雷射手術刀” (laser scalpel)
Examination (EXAM) The act of looking at or checking something carefully in order to discover possible diseases. “聽力檢查” (hearing test), “腦電波圖” (electroencephalography; EEG), “核磁共振造影” (magnetic resonance imaging; MRI)
Chemical (CHEM) Any basic chemical element typically found in the human body. “去氧核糖核酸” (deoxyribonucleic acid; DNA), “糖化血色素” (glycated hemoglobin), “膽固醇” (cholesterol), “尿酸” (uric acid)
Disease (DISE) An illness of people or animals caused by infection or a failure of health rather than by an accident. “小兒麻痺症” (poliomyelitis; polio), “帕金森氏症” (Parkinson’s disease), “青光眼” (glaucoma), “肺結核” (tuberculosis)
Drug (DRUG) Any natural or artificially made chemical used as a medicine “阿斯匹靈” (aspirin), “普拿疼” (acetaminophen), “青黴素” (penicillin), “流感疫苗” (influenza vaccination)
Supplement (SUPP) Something added to something else to improve human health. “維他命” (vitamin), “膠原蛋白” (collagen), “益生菌” (probiotics), “葡萄糖胺” (glucosamine), “葉黃素” (lutein)
Treatment (TREAT) A method of behavior used to treat diseases “藥物治療” (pharmacotherapy), “胃切除術” (gastrectomy), “標靶治療” (targeted therapy), “外科手術” (surgery)
Time (TIME) Element of existence measured in minutes, days, years “嬰兒期” (infancy), “幼兒時期” (early childhood), “青春期” (adolescence), “生理期” (on one’s period), “孕期” (pregnancy)

III. Data

● Training Set: Chinese HealthNER Corpus (Lee and Lu, 2021)
It includes 30,692 sentences with a total around 1.5 million characters or 91.7 thousand words. After manual annotation, we have 68,460 named entities across 10 entity types: body, symptom, instrument, examination, chemical, disease, drug, supplement, treatment, and time.

● Test set: at least 3,000 Chinese sentences will be provided for system performance evaluation.

The policy of this shared task is an open test. Participating systems are allowed to use other publicly available data for this shared task, but the use of other data should be specified in the final system description paper.

IV. Evaluation

The performance is evaluated by examining the difference between machine-predicted labels and human-annotated labels. We adopt standard precision, recall, and F1-score, which are the most typical evaluation metrics of NER systems at a character level. If the predicted tag of a character in terms of BIO format was completely identical with the gold standard, that is one of the defined BIO tags, the character in the testing instance was regarded as correctly recognized. Precision is defined as the percentage of named entities found by the NER system that are correct. Recall is the percentage of named entities present in the test set found by the NER system.

V. Important Dates

  • ● Release of training data: April 15, 2022
  • ● Release of test data: August 31, 2022
  • ● Testing results submission due: September 2, 2022
  • ● Release of evaluation results: September 5, 2022
  • ● System description paper due: September 20, 2022
  • ● Notification of Acceptance: September 30, 2022
  • ● Camera-ready deadline: October 7, 2022


  • Hai Leong Chieu, and Hwee Tou Ng (2003). Named entity recognition with a maximum approach. In Proceedings of 7th Conference on Natural Language Learning (CoNLL’03), pp. 160–163.
  • Jason P. C. Chiu, and Eric Nichols (2016). Named entity recognition with bidirectional LSTM-CNNs. Transactions of the Association for Computational Linguistics, 4:357–370.
  • Jingzhou He, and Houfeng Wang (2008). Chinese named entity recognition and word segmentation based on character. In Proceedings of the 6th SIGHAN Workshop on Chinese Language Processing (SIGHAN’08), pp. 128–132.
  • Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer (2016). Neural architectures for named entity recognition. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT’16), pp. 260–270.
  • Lung-Hao Lee, and Yi Lu (2021). Multiple Embeddings Enhanced Multi-Graph Neural Networks for Chinese Healthcare Named Entity Recognition. IEEE Journal of Biomedical and Health Informatics, 25(7): 2801- 2810.
  • Haibo Li, Masato Hagiwara, Qi Li, and Heng Ji (2014). Comparison of the impact of word segmentation on name tagging for Chinese and Japanese. In Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC’14), pp. 2532–2536.
  • Liyuan Liu, Jingbo Shang, Xiang Ren, Frank F. Xu, Huan Gui, Jian Peng, and Jiawei Han (2018). Empower sequence labeling with task-aware neural language model. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI’18), pp. 5253–5260.
  • Xuezhe Ma and Eduard Hovy (2016). End-to-end sequence labeling via Bi-directional LSTM-CNNs-CRF. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL’16), pp. 1064–1074.
  • Natalia Ponomareva, Ferran. Pla, Antonio Molina, and Paolo Rosso (2007). Biomedical named entity recognition: A poor knowledge HMM-based approach. In Proceedings of the 12th International Conference on Applications of Natural Language to Information Systems (NLDB’07), pp. 382–387.
  • Chih-Hsuan Wei, Robert Leaman, and Zhiyong Lu (2015). SimConcept: A hybrid approach for simplifying composite named entities in biomedical text. IEEE Journal of Biomedical and Health Informatics, 19(4):1385–1391.
  • Yue Zhang, and Jie Yang (2018). Chinese NER using lattice LSTM. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL’18), pp. 1554–1564.
  • Organization

    Honorary Chair

    • Chien-Huang Lin
    • Taipei Medical University

    Conference Chairs

    • Yung-Chun Chang
    • Taipei Medical University
    • Yi-Chin Huang
    • National Pingtung University

    Program Chairs

    • Jheng-Long Wu
    • Soochow University
    • Ming-Hsiang Su
    • Soochow University

    Demo Chairs

    • Hen-Hsen Huang
    • Academia Sinica

    Publication Chair

    • Yi-Fen Liu
    • Feng Chia University

    Shared Task Chair

    • Lung-Hao Lee
    • National Central University

    Special Session Chair

    • Chin-Hung Chou
    • National Central University
    • Yuan-Fu Liao
    • Taipei Tech Electronic University

    Organized by

    Taipei Medical University
    National Pingtung University

    Supported by

    Sponsored by



    Taipei city, Taiwan

    November 21 - 22, 2022

    09:00 AM – 05:00 PM

    343 Available Seats

    Hurryup! few tickets are left

    Free Lunch & Snacks

    Don’t miss it