Lidong Bing, Ph.D. from the Chinese University of Hong Kong, and post-doctoral fellow at Carnegie Mellon University. He is currently working in the Language Technology Laboratory of Alibaba DAMO Academy and is the head of the multilingual NLP team. Dr. Bing has been engaged in research and development in NLP for more than 10 years. His current research interests focus on low-resource NLP, sentiment analysis, text generation, etc. In recent years, he has published more than 100 papers in top artificial intelligence conferences and journals. He has participated in the organization and review of top NLP and machine learning journals and conferences as an associate editor, area chair, and senior program committee member.

Professor Tsering Gyal, Ph.D. supervisor, is the deputy director of the State Key Laboratory of Tibetan Language Intelligence Information Processing and Application, the deputy director of the Key Laboratory of Tibetan Language Information Processing of the Ministry of Education, the director of Qinghai Provincial Tibetan Information Processing Engineering Technology Research Center, the governmental counselor of Qinghai Province, the editorial board member of the Journal of Chinese Information.
He is currently engaged in researches in Tibetan information processing, machine translation, and Tibetan computational linguistics. He has won one first prize, two second prizes, and one third prize in provincial S&T Progress Award of Qinghai, one third prize of Qian Weichang Chinese information processing science and technology award. He also published more than 30 scientific research papers and 4 monographs.

Dr. Nan DUAN is a senior principal researcher & research manager at Microsoft Research Asia. He is an adjunct Ph.D. supervisor at University of Science and Technology of China and an adjunct professor at Tianjin University. He is a Distinguished Member of CCF. His research interests include natural language processing, code intelligence, multimodal intelligence, and machine reasoning.

Biao Zhang is a postdoc at the University of Edinburgh. He got his Ph.D. at the ILCC, University of Edinburgh, advised by Prof. Rico Sennrich and Prof. Ivan Titov. His research focuses on improving neural machine translation (NMT), particularly its efficiency and universality, including developing lightweight (fast and effective) architectures for NMT, low-resource NMT, massively multilingual NMT, speech-to-text translation, context-aware NMT, and their intersections. He has published several papers at top-tier NLP/ML conferences, such as ACL, EMNLP, NeurIPS, ICLR, ICML, AAAI, and IJCAI.

Deyi Xiong is a Professor of Computer Science at Tianjin University (TJU), Director of both the Natural Language Processing Laboratory at the College of Intelligence and Computing, TJU and the International Joint Research Center of Language Intelligence and Technology at TJU. Prior to joining TJU, he was a professor at Soochow University (2013-2018) and a research scientist at the Institute for Infocomm Research, Singapore (2007-2013). His research focuses on natural language processing, specifically machine translation, dialogue, natural language generation and commonsense reasoning. He has published over 100 papers in prestigious journals and conferences, including Computational Linguistics, IEEE TPAMI, IEEE TASLP, Artificial Intelligence, AAAI, IJCAI ACL, and EMNLP. He was the program co-chair of IALP 2021 and CWMT 2017. He has also served as an area chair of conferences including ACL, EMNLP, NAACL and COLING. He was the founder and co-organizer of multiple ACL/EMNLP/NAACL-affiliated workshops such as S2MT 2015, SedMT 2016 and DiscoMT 2019. He is a member of the standing committee of reviewers of CL, action editor of both TACL and ARR, and an editorial board member of International Journal of Asian Language Processing. He has been active in developing a variety of resources for natural language processing. He has recently led teams to create BiPaR that is a bilingual parallel novel-style machine reading comprehension dataset developed to support multilingual and cross-lingual reading comprehension, RiSAWOZ that is to date the largest fully annotated human-to-human task-oriented dialogue dataset, TED-CDB that is the largest PDTB-style Chinese discourse corpus built on a large set of TED talks, Chinese WPLC that is a Chinese dataset for evaluating pretrained language models on word prediction given long-range context.

Huang Degen is full professor of Nature Language Processing in the School of Computer Science and Technology at Dalian University of Technology. He holds MSc degrees in Computer Science and Engineering(1988，Dalian University of Technology), and a PhD in Computer Software and Theory(2003，Dalian University of Technology). He joined Dalian University of Technology in 1988, and was appointed full professor in 2006. He is a senior member of ACM, CAAI, ACL, CCF. He leads the Nature Language Processing and Machine Translation group. His research focus is on Machine Translation, with projects on machine translation in Cross-Language Information Retrieval (CLIR), on Multi-language Neural Machine Translation (MNMT). His research interests include developing nature language processing models and machine translation algorithms for solving Chinese-Japanese translation. He has published over 200 papers appeared at venues such as ACL, ACM, IEEE, COLING, EMNLP and a book about English shallow parsing for machine translation.

Guogen Cheng (senior engineer) is the Board Member, Vice President and Chief Technology Officer of GTCOM Technology Co., Ltd., Council Member of Chinese Information Society of China, Standing Committee Member of Multilingual Intelligent Information Processing Technology Committee of China Artificial Intelligence Society. He graduated from China University of Geosciences (Wuhan) majoring in computer science in 2000. He served as the technical director of Peking University Founder with a senior technical title (information system project manager, system architect), and one of the top ten digital talents of China Publishing Group. In the past 8 years, he has undertaken more than 10 national-level scientific research and industrial development projects from the Central Propaganda Department, the Ministry of Science and Technology, and the Ministry of Industry and Information Technology, and obtained more than 10 invention patents. He is one of principle investigators in the 2020 Science and Technology Innovation 2030 New Generation Artificial Intelligence Major Project - "Research on Multilingual Automatic Translation with Chinese as the Core", the project leader of the new generation artificial intelligence industry innovation task project of the Ministry of Industry and Information Technology.

Zhengshan Xue has been devoted to the research on machine translation & natural language processing and their industrial applications. He is leading the machine translation team in OPPO. His team has developed a variety of machine translation products embedded in OPPO platforms, benefiting over ten million users. He has published more than 10 papers, and has won the first place in multiple domestic and international machine translation evaluation campaigns (e.g., WMT, IWSLT, CCMT).