Computational discourse analysis addresses the rhetorical relationships across sentences. Although typical natural language processing (NLP) tasks have been shown their effectiveness with the rapid development of deep learning approaches, the tasks in discourse analysis that are highly associated with implicit contextual information are still challenging. This talk gives an overview of the recent advances in computational discourse analysis, in particular discourse relation recognition and discourse parsing, and shows how the discourse information provides clue to emerging AI topics, including argument mining, knowledge retrieval, clinical event extraction, and politeness-aware dialogue generation.
Dr. Hen-Hsen Huang is an assistant professor in the Department of Computer Science at the National Chengchi University. His research interests include natural language processing and information retrieval. His work has been published in ACL, SIGIR, WWW, IJCAI, CIKM, COLING, and other international conferences. Dr. Huang’s award and honors include the Honorable Mention of Doctoral Dissertation Award of ACLCLP in 2014 and the Honorable Mention of Master Thesis Award of ACLCLP in 2008. He served as the registration chair of TAAI 2017, the publication chair of ROCLING 2020, and as PC members of representative conferences in artificial intelligence including AAAI, IJCAI, ACL, COLING, EMNLP, and NAACL. He was one of organizers of FinNum Task at NTCIR-2014 and FinNLP Workshop at IJCAI 2019.