Inter-Active Learning with Queries on Instances and Features

In this talk, I will discuss a few projects aimed at “closing the loop” for interactive natural language annotation and learning. In particular, I describe two systems that combine active and semi-supervised learning by asking humans to label both instance queries (e.g., passages of text) and feature queries (e.g., advice about words or capitalization patterns, and the class labels they imply). Empirical results from real user studies show that these systems are better than state-of-the-art “passive” learning and even instance-only “active” learning, in terms of accuracy given a fixed budget of annotation time. The results are quite replicable and also provide insight into human annotator behavior, suggesting how human factors can and should be taken into account for interactive machine learning.

Speaker Details

Burr Settles is a staff research scientist and software engineer at Duolingo, an award-winning language education platform, where he has worked on numerous projects applying machine learning to language learning and testing. He also runs FAWM.ORG, an annual online collaborative songwriting experiment. Previously, he was a postdoc in machine learning at Carnegie Mellon University, and earned a PhD in computer sciences from the University of Wisconsin-Madison. His book Active Learning — an introduction to learning algorithms that are adaptive, curious, or exploratory (if you will) — was published by Morgan & Claypool in 2012. He gets around by bike and, among other things, plays guitar in the pop band Delicious Pastries.

Series:
Microsoft Research Talks
Date:
Speakers:
Burr Settles
Affiliation:
Duolingo

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Series: Microsoft Research Talks