LEARNING TO REJECT WITH A FIXED PREDICTOR: APPLICATION TO DECONTEXTUALIZATION

Christopher Mohri, Daniel Andor, Eunsol Choi, Michael Collins, Anqi Mao, Yutao Zhong

Research output: Contribution to conferencePaperpeer-review

Abstract

We study the problem of classification with a reject option for a fixed predictor, crucial to natural language processing. We introduce a new problem formulation for this scenario, and an algorithm minimizing a new surrogate loss function. We provide a complete theoretical analysis of the surrogate loss function with a strong H-consistency guarantee. For evaluation, we choose the decontextualization task, and provide a manually-labelled dataset of 2,000 examples. Our algorithm significantly outperforms the baselines considered, with a ∼25% improvement in coverage when halving the error rate, which is only ∼3% away from the theoretical limit.

Original languageEnglish (US)
StatePublished - 2024
Event12th International Conference on Learning Representations, ICLR 2024 - Hybrid, Vienna, Austria
Duration: May 7 2024May 11 2024

Conference

Conference12th International Conference on Learning Representations, ICLR 2024
Country/TerritoryAustria
CityHybrid, Vienna
Period5/7/245/11/24

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Science Applications
  • Education
  • Linguistics and Language

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