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1
AutoNLU: Detecting, root-causing, and fixing NLU model errors ...
Abstract: Improving the quality of Natural Language Understanding (NLU) models, and more specifically, task-oriented semantic parsing models, in production is a cumbersome task. In this work, we present a system called AutoNLU, which we designed to scale the NLU quality improvement process. It adds automation to three key steps: detection, attribution, and correction of model errors, i.e., bugs. We detected four times more failed tasks than with random sampling, finding that even a simple active learning sampling method on an uncalibrated model is surprisingly effective for this purpose. The AutoNLU tool empowered linguists to fix ten times more semantic parsing bugs than with prior manual processes, auto-correcting 65% of all identified bugs. ... : 8 pages, 5 figures ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences; I.2.7; Machine Learning cs.LG
URL: https://arxiv.org/abs/2110.06384
https://dx.doi.org/10.48550/arxiv.2110.06384
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2
A Novel Scheme for Speaker Recognition Using a Phonetically-Aware Deep Neural Network
In: DTIC (2014)
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3
The SRI NIST 2010 Speaker Recognition Evaluation System (PREPRINT)
In: DTIC (2011)
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4
State-of-the-art performance in text-independent speaker verification through open-source software
In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 15 (2007) 7, 1960-1968
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OLC Linguistik
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5
Gémellité et reconnaissance automatique du locuteur
In: Journées d'Etudes sur la Parole ; https://hal.archives-ouvertes.fr/hal-00134198 ; Journées d'Etudes sur la Parole, 2004, Fez, France. pp.445-448 (2004)
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