Active Learning via Membership Query Synthesis for Semi-Supervised Sentence Classification
| Autor: | Ines Rehbein, Raphael Schumann |
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| Rok vydání: | 2019 |
| Předmět: |
Training set
Active learning (machine learning) business.industry Computer science 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Task (project management) Annotation Manual annotation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Natural language processing Sentence 0105 earth and related environmental sciences |
| Zdroj: | CoNLL |
| DOI: | 10.18653/v1/k19-1044 |
| Popis: | 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 |
| Databáze: | OpenAIRE |
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