Active Learning via Membership Query Synthesis for Semi-Supervised Sentence Classification

Raphael Schumann, Ines Rehbein


Abstract
Active learning (AL) is a technique for reducing manual annotation effort during the annotation of training data for machine learning classifiers. For NLP tasks, pool-based and stream-based sampling techniques have been used to select new instances for AL while gen erating new, artificial instances via Membership Query Synthesis was, up to know, considered to be infeasible for NLP problems. We present the first successfull attempt to use Membership Query Synthesis for generating AL queries, using Variational Autoencoders for query generation. We evaluate our approach in a text classification task and demonstrate that query synthesis shows competitive performance to pool-based AL strategies while substantially reducing annotation time
Anthology ID:
K19-1044
Volume:
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
472–481
URL:
https://www.aclweb.org/anthology/K19-1044
DOI:
10.18653/v1/K19-1044
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PDF:
https://www.aclweb.org/anthology/K19-1044.pdf