Intent Detection-Based Lithuanian Chatbot Created via Automatic DNN Hyper-Parameter Optimization

In this paper, we tackle an intent detection problem for the Lithuanian language with the real supervised data. Our main focus is on the enhancement of the Natural Language Understanding (NLU) module, responsible for the comprehension of user's questions. The NLU model is trained with a properly selected word vectorization type and Deep Neural Network (DNN) classifier. During our experiments, we have experimentally investigated fastText and BERT embeddings. Besides, we have automatically optimized different architectures and hyper-parameters of the following DNN approaches: Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM) and Convolutional Neural Network (CNN). The highest accuracy=similar to 0.715 (similar to 0.675 and similar to 0.625 over random and majority baselines, respectively) was achieved with the CNN classifier applied on a top of BERT embeddings. The detailed error analysis revealed that prediction accuracies degrade for the least covered intents and due to intent ambiguities; therefore, in the future, we are planning to make necessary adjustments to boost the intent detection accuracy for the Lithuanian language even more.