A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets

• URL: http://arxiv.org/abs/2202.10137v1
• Date: Mon, 21 Feb 2022 11:36:19 GMT
• Title: A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets
• Authors: Zvi Kons, Aharon Satt, Hong-Kwang Kuo, Samuel Thomas, Boaz Carmeli, Ron Hoory, Brian Kingsbury
• Abstract summary: We present the Nearest Neighbors Scores Improvement (NNSI) algorithm for automatic data selection and labeling. The NNSI reduces the need for manual labeling by automatically selecting highly-ambiguous samples and labeling them with high accuracy. We demonstrated the use of NNSI on two large-scale, real-life voice conversation systems.
• Score: 23.495743195811375
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Intent classifiers are vital to the successful operation of virtual agent systems. This is especially so in voice activated systems where the data can be noisy with many ambiguous directions for user intents. Before operation begins, these classifiers are generally lacking in real-world training data. Active learning is a common approach used to help label large amounts of collected user input. However, this approach requires many hours of manual labeling work. We present the Nearest Neighbors Scores Improvement (NNSI) algorithm for automatic data selection and labeling. The NNSI reduces the need for manual labeling by automatically selecting highly-ambiguous samples and labeling them with high accuracy. This is done by integrating the classifier's output from a semantically similar group of text samples. The labeled samples can then be added to the training set to improve the accuracy of the classifier. We demonstrated the use of NNSI on two large-scale, real-life voice conversation systems. Evaluation of our results showed that our method was able to select and label useful samples with high accuracy. Adding these new samples to the training data significantly improved the classifiers and reduced error rates by up to 10%.

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