Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates

Artem Shelmanov, Dmitri Puzyrev, Lyubov Kupriyanova, Denis Belyakov, Daniil Larionov, Nikita Khromov, Olga Kozlova, Ekaterina Artemova, Dmitry V. Dylov, Alexander Panchenko


Abstract
Annotating training data for sequence tagging of texts is usually very time-consuming. Recent advances in transfer learning for natural language processing in conjunction with active learning open the possibility to significantly reduce the necessary annotation budget. We are the first to thoroughly investigate this powerful combination for the sequence tagging task. We conduct an extensive empirical study of various Bayesian uncertainty estimation methods and Monte Carlo dropout options for deep pre-trained models in the active learning framework and find the best combinations for different types of models. Besides, we also demonstrate that to acquire instances during active learning, a full-size Transformer can be substituted with a distilled version, which yields better computational performance and reduces obstacles for applying deep active learning in practice.
Anthology ID:
2021.eacl-main.145
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1698–1712
Language:
URL:
https://aclanthology.org/2021.eacl-main.145
DOI:
10.18653/v1/2021.eacl-main.145
Bibkey:
Cite (ACL):
Artem Shelmanov, Dmitri Puzyrev, Lyubov Kupriyanova, Denis Belyakov, Daniil Larionov, Nikita Khromov, Olga Kozlova, Ekaterina Artemova, Dmitry V. Dylov, and Alexander Panchenko. 2021. Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1698–1712, Online. Association for Computational Linguistics.
Cite (Informal):
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates (Shelmanov et al., EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.145.pdf
Data
CoNLL-2003