Applying Deep Learning to Answer Selection: A Study and An Open Task
This work addresses question answering for insurance applications, but it is incremental as it applies existing deep learning techniques to a new domain.
The authors tackled non-factoid question answering in the insurance domain by applying a deep learning framework without linguistic tools, achieving a top-1 accuracy of 65.3% on a test set and outperforming baseline methods.
We apply a general deep learning framework to address the non-factoid question answering task. Our approach does not rely on any linguistic tools and can be applied to different languages or domains. Various architectures are presented and compared. We create and release a QA corpus and setup a new QA task in the insurance domain. Experimental results demonstrate superior performance compared to the baseline methods and various technologies give further improvements. For this highly challenging task, the top-1 accuracy can reach up to 65.3% on a test set, which indicates a great potential for practical use.