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• W4224935278 abstract "We propose a new Customer Satisfaction Estimation (CSE) method that utilizes unsupervised representation learning. Though conventional methods have improved both the heuristic features and the estimation models, their performance is still insufficient as only small amounts of labeled training data can be expected. To mitigate this problem, the proposed method leverages a large amount of unlabeled data by unsupervised representation learning based on self-training. The key advance of the proposed method is to introduce a Multi-Format Prediction (MFP) loss to improve the performance of self-training for the inputs that contain both continuous and biased discrete features such as the number of occurrences of a particular word. MFP loss uses two loss functions based on regression and weighted binary classification to reconstruct both types of features with high accuracy. Experiments on real English contact center calls reveal the improved CSE performance attained by the proposed method." @default.
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• W4224935278 date "2022-05-23" @default.
• W4224935278 modified "2023-09-26" @default.
• W4224935278 title "Customer Satisfaction Estimation Using Unsupervised Representation Learning with Multi-Format Prediction Loss" @default.
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• W4224935278 doi "https://doi.org/10.1109/icassp43922.2022.9747482" @default.
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