Host: Dragomir Radev
Coffee available at 3:45
Title: Web-scale Neural Memory towards Universal Knowledge Interface
Modern natural language tasks are increasingly dependent on external world knowledge. My PhD study has particularly focused on three challenges in this literature: handling unstructured knowledge, being scalable, and reasoning over knowledge data. I will mainly discuss my recent and on-going work on a web-scale neural memory that tackles all of the three challenges, and show how it serves as an effective interface for interacting with the world knowledge. I will conclude with an argument that designing a seamless and universal knowledge interface is a crucial research goal that can better address knowledge-dependency problem in machine learning tasks.
Minjoon Seo is a final-year Ph.D. student in the Allen School of Computer Science & Engineering at the University of Washington, advised by Hannaneh Hajishirzi and Ali Farhadi. His research interest has been mostly in the learning model for the extraction of (IE), the access to (QA), and the interplay of (Reasoning) knowledge in various forms of language data. He is supported by Facebook Fellowship and AI2 Key Scientific Challenges Award. He co-organizes the Workshop on Machine Reading for Question Answering (MRQA) and the Workshop on Representation Learning for NLP (RepL4NLP).