This talk is part of the NLP Seminar Series.

From Data to Model Programming: Injecting Structured Priors for Knowledge Extraction

Xiang Ren, University of Southern California
Date: 11:00 am - 12:00 pm, May 9 2019
Venue: Room 104, Gates Computer Science Building


Deep neural models have achieved state-of-the-art performance on knowledge extraction tasks, from sequence tagging, to relation extraction, and knowledge reasoning. Yet, these data-hungry models have heavy reliance on human-labeled training data and often operate as "black-box" components, slowing down the development of downstream applications.

In this talk, I will introduce our recent advances on imposing structured prior knowledge into deep neural models for knowledge extraction, both at the input data level (i.e., programming the "data") and at the model architecture level (i.e., programming the "model"). In particular, I will discuss how to faithfully incorporate domain dictionaries in named entity recognition and how to softly ground semantic rules in relation extraction, as examples of input-level structured priors. Towards model programming, I will present a graph neural network-based framework, for capturing multi-relational structured priors as part of the model architecture in entity recognition and commonsense reasoning. Together, these solutions form a roadmap for going from "data" to "model" programming with structured priors.


Xiang Ren is an assistant professor of Computer Science at USC with affiliated appointment at USC ISI. He is also the director of Intelligence and Knowledge Discovery (INK) Research Lab, the Information Director of ACM SIGKDD and Data Mining (SIGKDD), and member of USC Machine Learning Center. Priorly, he was a research scholar at Stanford University, and received his Ph.D. in Computer Science from University of Illinois Urbana-Champaign. Dr. Ren’s research focuses on developing label-efficient computational methods that extract machine-actionable knowledge (e.g., compositional, graph-structured representations) from natural-language data, as well as performing neural reasoning over the knowledge structures. His research leads to a book and over 50 publications, was covered in over 10 conference tutorials (KDD, WWW, NAACL), and received awards including Google AI Faculty Award, JP Morgan AI Research Award, Amazon Research Award, ACM SIGKDD Dissertation Award (2018), WWW Best Poster runner-up (2018), David J. Kuck Outstanding Thesis Award (2017), Google PhD fellowship (2016), and Yelp Dataset Challenge Award (2015). He's part of the Forbes' Asia 30 Under 30.