Multi-task active learning for linguistic annotations

Roi Reichart*, Katrin Tomanek, Udo Hahn, Ari Rappoport

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

69 Scopus citations

Abstract

We extend the classical single-task active learning (AL) approach. In the multi-task active learning (MTAL) paradigm, we select examples for several annotation tasks rather than for a single one as usually done in the context of AL. We introduce two MTAL metaprotocols, alternating selection and rank combination, and propose a method to implement them in practice. We experiment with a twotask annotation scenario that includes named entity and syntactic parse tree annotations on three different corpora. MTAL outperforms random selection and a stronger baseline, onesided example selection, in which one task is pursued using AL and the selected examples are provided also to the other task.

Original languageAmerican English
Title of host publicationACL-08
Subtitle of host publicationHLT - 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference
Pages861-869
Number of pages9
StatePublished - 2008
Event46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-08: HLT - Columbus, OH, United States
Duration: 15 Jun 200820 Jun 2008

Publication series

NameACL-08: HLT - 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference

Conference

Conference46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-08: HLT
Country/TerritoryUnited States
CityColumbus, OH
Period15/06/0820/06/08

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