Learning Health-Bots from Training Data that was Automatically Created using Paraphrase Detection and Expert Knowledge
Anna Liednikova, Philippe Jolivet, Alexandre Durand-Salmon, Claire Gardent · 2020
A key bottleneck for developing dialog models is the lack of adequate training data.Due to privacy issues, dialog data is even scarcer in the health domain.We propose a novel method for creating dialog corpora which we apply to create doctor-patient interaction data.We use this data to learn both a generation and a hybrid classification/retrieval model and find that the generation model consistently outperforms the hybrid model.We show that our data creation method has several advantages.Not only does it allow for the semi-automatic creation of large quantities of training data.It also provides a natural way of guiding learning and a novel method for assessing the quality of human-machine interactions.