Title: Learning Deep Neural Networks for Hard Natural Language Problems
Abstract: Deep learning has revolutionized the way that many tasks are tackled in Natural Language Processing (NLP). This talk will cover some recent advances in deep learning for NLP, including embeddings, encoder-decoder architecture, attentions, and language modeling. Then, I will introduce some of the research work in the Monash NLP Group, which focuses on learning deep neural models for real-world NLP problems. Some future NLP research directions will be mentioned in the end.
Biography: Ming Liu is a lecturer in the Department of Artificial Intelligence and Data Science at Monash University, Australia. He graduated from Monash University with a Ph.D. in 2019. His main research interests are NLP, Deep Learning, Medical Data Analysis, Active Learning, and Weakly Supervised Learning. He is currently teaching Applied Data Analysis, Data Mining, and NLP at Monash University. At present, his main projects include the Melbourne Alfred Hospital project “Artificial Intelligence Automatic Fungal Disease Surveillance”, and the China National Books Import and Export Corporation project “Technical Research on Automatic Summarization of Scientific Articles and Books”. At the same time, he also serves as a reviewer for conferences in the field of NLP such as ACL and EMNLP.
Time: 10: 00am, Monday, January 6, 2020
Venue: School of Artificial Intelligence (Room 601, Administration Building, Central Campus)
Organizer: School of Artificial Intelligence
