Unseen Entity Handling in Complex Question Answering over Knowledge Base via Language Generation
Xin Huang, Jung‐Jae Kim, Bowei Zou · 2021
Complex question answering over knowledge base remains as a challenging task because it involves reasoning over multiple pieces of information, including intermediate entities/relations and other constraints.Previous methods simplify the SPARQL query of a question into such forms as a list or a graph, missing such constraints as "filter" and "order_by", and present models specialized for generating those simplified forms from a given question.We instead introduce a novel approach that directly generates an executable SPARQL query without simplification, addressing the issue of generating unseen entities.We adapt large scale pre-trained encoder-decoder models and show that our method significantly outperforms the previous methods and also that our method has higher interpretability and computational efficiency than the previous methods.