I am a PhD student in Computer Science at UCLA working with Prof. Wei Wang. I am interested in extracting structured knowledge from unstructed text using generative language models. Specifically, my research unbinds structure prediction models from constraints of data resources, label ontology, document structure, and domain.
I earned my bachelor’s degree in Computing from The Hong Kong Polytechnic University with First Class Honours in 2018, advised by Prof. Qin Lu and Prof. Jiannong Cao. I studied as an exchange student at the University of Maryland in 2016. I’ve also spent time at Amazon Alexa AI (working with Dr. Jiun-Yu Kao and Dr. Tagyoung Chung), USC Information Sciences Institute (working with Prof. Nanyun (Violet) Peng and Prof. Muhao Chen), The Chinese University of Hong Kong (working with Prof. Helen Meng), UC Santa Cruz (working with Prof. Marilyn Walker) and MIT (working with Dr. Abel Sanchez and Prof. John R. Williams).
|Aug 24 Thu, 10:00-10:20 (IST)||Wicklow Hall 1||Oral presentation of the conf paper: Parameter-Efficient Low-Resource Dialogue State Tracking by Prompt Tuning. In the collaboration work with Amazon Alexa AI, we introduce a dialogue state tracking model tuning less than 1% of LM parameters and achieves better low-resource performance with prompt tuning techniques.|
|July 10, 11:00-12:30 (EDT)||Metropolitan East||Oral presentation of the main conf paper: DICE: Data-Efficient Clinical Event Extraction with Generative Models. We introduce a data-efficient generative model with specialized mention boundary predictions for clinical event extraction. We also propose MACCROBAT-EE, the first benchmark in the domain.|
|July 10, 19:00-21:00 (EDT)||Metropolitan Centre||Findings Spotlights of Multi-hop Evidence Retrieval for Cross-document Relation Extraction. We propose a multi-hop evidence retrieval method based on evidence path mining and ranking with dense retrievers, which shows great cross-document relation extraction performance.|
|July 11, 16:15-17:45 (EDT)||Metropolitan Centre||Oral presentation of the main conf paper: Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction? We leverage additional cross-domain supervision signals from Natural Language Inference on the Relation Extraction task. We show that it achieves state-of-the-art biomedical RE performance.|
|July 14, 11:40-12:30 (EDT)||–||Poster presentation at the TrustNLP: Third Workshop on Trustworthy Natural Language Processing|
University of Maryland, College Park
2016, College Park, MD