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Intent Detection Problem Solving via Automatic DNN Hyperparameter Optimization by KapoÄiÅ«tÄ-DzikienÄ, Jurgita; Balodis, Kaspars; SkadiÅÅ¡, Raivis is a Engineering article available to read on EtoBox.

What is Intent Detection Problem Solving via Automatic DNN Hyperparameter Optimization about?

Accurate intent detection-based chatbots are usually trained on larger datasets that are not available for some languages. Seeking the most accurate models, three English benchmark datasets that were human-translated into four morphologically complex languages (i.e., Estonian, Latvian, Lithuanian, Russian) were used. Two types of word embeddings (fastText and BERT), three types of deep neural network (DNN) classifiers (convolutional neural network (CNN); long short-term memory method (LSTM), and bidirectional LSTM (BiLSTM)), different DNN architectures (shallower and deeper), and various DNN hyperparameter values were investigated. DNN architecture and hyperparameter values were optimized automatically using the Bayesian method and random search. On three datasets of 2/5/8 intents for English, Estonian, Latvian, Lithuanian, and Russian languages, accuracies of 0.991/0.890/0.712, 0.972/0.890/0.644, 1.000/0.890/0.644, 0.981/0.872/0.712, and 0.972/0.881/0.661 were achieved, respectively. The BERT multilingual vectorization with the CNN classifier was proven to be a good choice for all datasets for all languages. Moreover, in the majority of models, the same set of optimal hyperparamet

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Author

KapoÄiÅ«tÄ-DzikienÄ, Jurgita; Balodis, Kaspars; SkadiÅÅ¡, Raivis

Publisher

MDPI AG; Multidisciplinary Digital Publishing Institute (MDPI); Basel: MDPI AG, 2011- (ISSN 2076-3417)

Published

2020

Language

EN

Field

Engineering (Physical Sciences)