Paper: Multi-Task Active Learning for Linguistic Annotations

ACL ID P08-1098
Title Multi-Task Active Learning for Linguistic Annotations
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008

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