Resumen de Interactive Learning of Dialog Scenarios from Examples
This paper reports on the development of a toolkit that enables collecting dialog corpus for end-to-end goal-oriented dialog system training. The toolkit includes the neural network model that interactively learns to predict the next virtual assistant (VA) action from the conversation history. We start with exploring methods for VA dialog scenario learning from examples after we perform several experiments with the English DSTC dialog sets in order to find the optimal strategy for neural model training. The chosen algorithm is used for training the next action prediction model for the Latvian dialogs in the public transport inquiries domain collected using the platform. The accuracy for the English and the Latvian dialog models is similar – 0.84 and 0.86. This shows that the chosen method for neural network model training is language independent.
