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        <title>Active Learning via Membership Query Synthesis for Semi-Supervised Sentence Classification</title>
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        <namePart type="given">Raphael</namePart>
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            <title>Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)</title>
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        <name type="personal">
            <namePart type="given">Mohit</namePart>
            <namePart type="family">Bansal</namePart>
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        <name type="personal">
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    <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</abstract>
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    <identifier type="doi">10.18653/v1/K19-1044</identifier>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T Active Learning via Membership Query Synthesis for Semi-Supervised Sentence Classification
%A Schumann, Raphael
%A Rehbein, Ines
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F schumann-rehbein-2019-active
%X 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
%R 10.18653/v1/K19-1044
%U https://aclanthology.org/K19-1044
%U https://doi.org/10.18653/v1/K19-1044
%P 472-481

Markdown (Informal)

[Active Learning via Membership Query Synthesis for Semi-Supervised Sentence Classification](https://aclanthology.org/K19-1044) (Schumann & Rehbein, CoNLL 2019)

• Active Learning via Membership Query Synthesis for Semi-Supervised Sentence Classification (Schumann & Rehbein, CoNLL 2019)

ACL

• Raphael Schumann and Ines Rehbein. 2019. Active Learning via Membership Query Synthesis for Semi-Supervised Sentence Classification. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 472–481, Hong Kong, China. Association for Computational Linguistics.