MDP-based Shallow Parsing in Distantly Supervised QA Systems

Question answering systems over knowledge graphs commonly consist of multiple components such as shallow parser, entity/relation linker, query generation and answer retrieval. We focus on the first task, shallow parsing, which so far received little attention in the QA community. Despite the lack of gold annotations for shallow parsing in question answering datasets, we devise a Reinforcement Learning based model called MDP-Parser, and show that it outperforms the current state-of-the-art approaches. Furthermore, it can be easily embedded into the existing entity/relation linking tools to boost the overall accuracy.