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arXiv · 1810.03450

Active Learning for New Domains in Natural Language Understanding

Stanislav Peshterliev·John Kearney·Abhyuday Jagannatha·Imre Kiss·Spyros Matsoukas
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Abstract

We explore active learning (AL) for improving the accuracy of new domains in a natural language understanding (NLU) system. We propose an algorithm called Majority-CRF that uses an ensemble of classification models to guide the selection of relevant utterances, as well as a sequence labeling model to help prioritize informative examples. Experiments with three domains show that Majority-CRF achieves 6.6%-9% relative error rate reduction compared to random sampling with the same annotation budget, and statistically significant improvements compared to other AL approaches. Additionally, case studies with human-in-the-loop AL on six new domains show 4.6%-9% improvement on an existing NLU system.

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Stanislav Peshterliev, John Kearney, Abhyuday Jagannatha, Imre Kiss, Spyros Matsoukas. 2018-10-03. Active Learning for New Domains in Natural Language Understanding. https://arxiv.org/abs/1810.03450

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