Natural language understanding for task oriented dialog in the biomedical domain in a low resources context [article]

Antoine Neuraz, Leonardo Campillos Llanos, Anita Burgun, Sophie Rosset
2018 arXiv   pre-print
In the biomedical domain, the lack of sharable datasets often limit the possibility of developing natural language processing systems, especially dialogue applications and natural language understanding models. To overcome this issue, we explore data generation using templates and terminologies and data augmentation approaches. Namely, we report our experiments using paraphrasing and word representations learned on a large EHR corpus with Fasttext and ELMo, to learn a NLU model without any
more » ... able dataset. We evaluate on a NLU task of natural language queries in EHRs divided in slot-filling and intent classification sub-tasks. On the slot-filling task, we obtain a F-score of 0.76 with the ELMo representation; and on the classification task, a mean F-score of 0.71. Our results show that this method could be used to develop a baseline system.
arXiv:1811.09417v2 fatcat:rgxvew5cizgbxmwmv4n6w5gpwq