Structured output learning with indirect supervision

Ming Wei Chang, Vivek Srikumar, Dan Goldwasser, Dan Roth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a novel approach for structure prediction that addresses the difficulty of obtaining labeled structures for training. We observe that structured output problems often have a companion learning problem of determining whether a given input possesses a good structure. For example, the companion problem for the part-of-speech (POS) tagging task asks whether a given sequence of words has a corresponding sequence of POS tags that is "legitimate". While obtaining direct supervision for structures is difficult and expensive, it is often very easy to obtain indirect supervision from the companion binary decision problem. In this paper, we develop a large margin framework that jointly learns from both direct and indirect forms of supervision. Our experiments ex-hibit the significant contribution of the easy-to-get indirect binary supervision on three important NLP structure learning problems.

Original languageEnglish (US)
Title of host publicationICML 2010 - Proceedings, 27th International Conference on Machine Learning
Pages199-206
Number of pages8
StatePublished - Sep 17 2010
Event27th International Conference on Machine Learning, ICML 2010 - Haifa, Israel
Duration: Jun 21 2010Jun 25 2010

Publication series

NameICML 2010 - Proceedings, 27th International Conference on Machine Learning

Other

Other27th International Conference on Machine Learning, ICML 2010
CountryIsrael
CityHaifa
Period6/21/106/25/10

ASJC Scopus subject areas

  • Artificial Intelligence
  • Education

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