Large-Scale Question Answering with Joint Embedding and Proof Tree Decoding

Question answering (QA) over a large-scale knowledge base (KB) such as Freebase is an important natural language processing application. There are linguistically oriented semantic parsing techniques and machine learning motivated statistical methods. Both of these approaches face a key challenge on how to handle diverse ways natural questions can be expressed about predicates and entities in the KB. This paper is to investigate how to combine these two approaches. We frame the problem from a proof-theoretic perspective, and formulate it as a proof tree search problem that seamlessly unifies semantic parsing, logic reasoning, and answer ranking. We combine our word entity joint embedding learned from web-scale data with other surface-form features to further boost accuracy improvements. Our real-time system on the Freebase QA task achieved a very high F1 score (47.2) on the standard Stanford WebQuestions benchmark test data.