An Embedding-Based Approach to Repairing Question Semantics

A question with complete semantics can be answered correctly. In other words, it contains all the basic semantic elements. In fact, the problem is not always complete due to the ambiguity of the user's intentions. Unfortunately, there is very little research on this issue. In this paper, we present an embedding-based approach to completing question semantics by inspiring from knowledge graph completion based on our proposed representation of a complete basic question as unique type and subject and multiple possible constraints. Firstly, we propose a back-and-forth-based matching method to acknowledge the question type as well as a word2vec-based method to extract all constraints via question subject and its semantic relevant in knowledge bases. Secondly, we introduce a time-aware recommendation to choose the best candidates from vast possible constraints for capturing users' intents precisely. Finally, we present constraint-independence-based attention to generate complete questions naturally. Experiments verifies the effectiveness of our approach.