Call for Participation: Shared Tasks in NLPCC 2021 The 10th CCF International Conference on Natural Language Processing and Chinese Computing (NLPCC2021) Registration Deadline: May 30th，2021 http://tcci.ccf.org.cn/conference/2021/ NLPCC 2021 will follow the NLPCC tradition of holding several shared tasks in natural language processing and Chinese computing. This year’s shared tasks focus on both classical problems and newly emerging problems, including Argumentative Text Understanding for AI Debater, Few-shot Learning for Chinese Language Understand Evaluation with the Pre-training Language Model and Sub Event Identification. Participants from both academia and industry are welcomed. Each group can participate in one or multiple tasks and members in each group can attend the NLPCC conference to present their techniques and results. The participants will be invited to submit papers to the main conference and the accepted papers will appear in the conference proceedings published by Springer LNCS. The top 3 participating teams of each task will be certificated by NLPCC and CCF-NLP. If a task has multiple sub-tasks or tracks, then only the top 1 participating team of each sub-task/track will be certificated. 1. Overview of the Shared Tasks There are three shared tasks in this year’s NLPCC conference and the detailed description of each task can be found in the task guidelines released later. Here we only give a brief overview of each task. Task 1 - Argumentative Text Understanding for AI Debater (AIDebater) Argument and debate are fundamental capabilities of human intelligence, essential for a wide range of human activities, and common to all human societies. With an aim of developing an autonomous debating system, we make an initial step to understand argumentative text of debating in this shared task, including three tracks, namely, supporting material identification (track 1), argument pair identification from online forum (track 2) and argument pair extraction from peer review and rebuttal (track 3). In track 1, we present the fundamental scenario of supporting material identification for a given debating topic. We then move to the understanding of dialogical argumentative text in two domains, i.e., online debating forum and scientific paper review process. We provide three datasets in this task, one for each track. Organizer: Fudan University, Alibaba Group. Contact: aidebater@163.com Task 2 - FewCLUE: Few-shot Learning for Chinese Language Understanding Evaluation with the Pre-trained Language Model Pretrained language models have greatly improved the performance of various NLP tasks among many fields. With the help of many labelled data, people could just make a little bit fine-tuning of a pre-trained language model to get a great performance for downstream tasks compared with the age without a pre-trained language model. However, humanity could learn a new concept with only a few samples. Few-shot learning is the one to study how a machine could learn a new concept with only a few data. The goal of this task is to explore the capability of a pretrained language model conditioned on a few training data for the downstream task. CLUE designed this task based on CLUE benchmark, wishes to promote more research and applications in Chinese NLP few-shot learning. Organizer: CLUE benchmark (www.CLUEbenchmarks.com) Contact: cluebenchmark@163.com and Junyi Li (4ljy@163.com) Task 3 - Automatic Information Extraction – Sub Event Identification (AutoIE 2) Sub-events identification is a very fundamental problem in the field of information extraction, especially in emergency situations (e.g., terrorist attacks). Events usually evolve rapidly and therefore successive sub-events occur. How to efficiently identify the target sub-events from large volume of related data is very challenging. The goal of this task is to build an IE system that can quickly adapt to a new occurring sub-event. Specifically, given a large event-related corpus and a few labelled seed data, the task aims to build an IE system capable of identifying the target sub-events. Besides machine learning model designing, selecting data from the unlabeled corpus and annotating them is also allowed, but the size of the labelled data is fixed. How to select the best data to annotate is also an important step in this task. The task setting is very practical and thus the proposed solutions may generalize well in real world applications. Organizer: TSinghua SIGS Contact: Xingyu Bai (bxy20@mails.tsinghua.edu.cn) and Xuefeng Yang (yang0302@e.ntu.edu.sg) 2. How to Participate Task 1 - Argumentative Text Understanding for AI Debater (AIDebater) Registration website: http://www.fudan-disc.com/sharedtask/AIDebater21/index.html Task 2 - FewCLUE: Few-shot Learning for Chinese Language Understanding Evaluation with the Pre-trained Language Model Registration website: https://www.cluebenchmarks.com/NLPCC.html Task 3 - Automatic Information Extraction – Sub Event Identification (AutoIE 2) Please fill out the Shared Task 3 Registration Form (Word File) and send it to the following registration email. Registration Email: bxy20@mails.tsinghua.edu.cn 3. Important dates 2021/04/10：announcement of shared tasks and call for participation; 2021/04/10：registration open; 2021/04/30：release of detailed task guidelines & training data; 2021/05/30：registration deadline; 2021/06/05：release of test data; 2021/06/15：participants’ results submission deadline; 2021/06/25：evaluation results release and call for system reports and conference paper; 2021/07/15：conference paper submission deadline (only for shared tasks); 2021/07/30：conference paper accept/reject notification; 2021/08/14：camera-ready paper submission deadline. 4. Shared Task Organizers (in alphabetical order) Xingyu Bai, TSinghua SIGS Lidong Bing, Alibaba Group Yunbo Cao, Tencent Liying Cheng , Alibaba Group Weigang Guo, TSinghua SIGS Ruidan He , Alibaba Group Junyi Li, CLUE benchmark Yinzi Li, Fudan University Qin Liu, Fudan University Chenhui Shen, Alibaba Group Zhongyu Wei, Fudan University Huilin Xu, CLUE benchmark Liang Xu, CLUE benchmark Xuefeng Yang, TSinghua SIGS Yujiu Yang, TSinghua SIGS Jian Yuan, Fudan University Jiajun Zhang, Institute of Automation Chinese Academy of Sciences