Timezone: »
We consider the problem of wisely using a limited budget to label a small subset of a large unlabeled dataset. For example, consider the NLP problem of word sense disambiguation. For any word, we have a set of candidate labels from a knowledge base, but the label set is not necessarily representative of what occurs in the data: there may exist labels in the knowledge base that very rarely occur in the corpus because the sense is rare in modern English; and conversely there may exist true labels that do not exist in our knowledge base. Our aim is to obtain a classifier that performs as well as possible on examples of each “common class” that occurs with frequency above a given threshold in the unlabeled set while annotating as few examples as possible from “rare classes” whose labels occur with less than this frequency. The challenge is that we are not informed which labels are common and which are rare, and the true label distribution may exhibit extreme skew. We describe an active learning approach that (1) explicitly searches for rare classes by leveraging the contextual embedding spaces provided by modern language models, and (2) incorporates a stopping rule that ignores classes once we prove that they occur below our target threshold with high probability. We prove that our algorithm only costs logarithmically more than a hypothetical approach that knows all true label frequencies and show experimentally that incorporating automated search can significantly reduce the number of samples needed to reach target accuracy levels.
Author Information
Jason Hartford (University of British Columbia)
Kevin Leyton-Brown (University of British Columbia)
Hadas Raviv (AI21 Labs)
Dan Padnos (AI21 Labs)
Shahar Lev (AI21 Labs)
Barak Lenz (AI21 Labs)
More from the Same Authors
-
2020 Poster: ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool »
Gellert Weisz · András György · Wei-I Lin · Devon Graham · Kevin Leyton-Brown · Csaba Szepesvari · Brendan Lucier -
2019 : Coffee break, posters, and 1-on-1 discussions »
Julius von Kügelgen · David Rohde · Candice Schumann · Grace Charles · Victor Veitch · Vira Semenova · Mert Demirer · Vasilis Syrgkanis · Suraj Nair · Aahlad Puli · Masatoshi Uehara · Aditya Gopalan · Yi Ding · Ignavier Ng · Khashayar Khosravi · Eli Sherman · Shuxi Zeng · Aleksander Wieczorek · Hao Liu · Kyra Gan · Jason Hartford · Miruna Oprescu · Alexander D'Amour · Jörn Boehnke · Yuta Saito · Théophile Griveau-Billion · Chirag Modi · Shyngys Karimov · Jeroen Berrevoets · Logan Graham · Imke Mayer · Dhanya Sridhar · Issa Dahabreh · Alan Mishler · Duncan Wadsworth · Khizar Qureshi · Rahul Ladhania · Gota Morishita · Paul Welle -
2019 Poster: Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration »
Robert Kleinberg · Kevin Leyton-Brown · Brendan Lucier · Devon Graham -
2016 Poster: Deep Learning for Predicting Human Strategic Behavior »
Jason Hartford · James R Wright · Kevin Leyton-Brown -
2016 Oral: Deep Learning for Predicting Human Strategic Behavior »
Jason Hartford · James R Wright · Kevin Leyton-Brown -
2010 Poster: Bayesian Action-Graph Games »
Albert Xin Jiang · Kevin Leyton-Brown