Formal Query Building with Query Structure Prediction for Complex Question Answering over Knowledge Base
This work addresses the challenge of improving accuracy in complex question answering over knowledge bases, which is incremental as it builds on existing ranking methods by adding a structure prediction step.
The paper tackles the problem of generating noisy candidate queries in formal query building for complex question answering over knowledge bases by proposing a two-stage approach that first predicts query structure to constrain generation and then ranks candidates, resulting in outperformance on complex questions while remaining competitive on simple ones.
Formal query building is an important part of complex question answering over knowledge bases. It aims to build correct executable queries for questions. Recent methods try to rank candidate queries generated by a state-transition strategy. However, this candidate generation strategy ignores the structure of queries, resulting in a considerable number of noisy queries. In this paper, we propose a new formal query building approach that consists of two stages. In the first stage, we predict the query structure of the question and leverage the structure to constrain the generation of the candidate queries. We propose a novel graph generation framework to handle the structure prediction task and design an encoder-decoder model to predict the argument of the predetermined operation in each generative step. In the second stage, we follow the previous methods to rank the candidate queries. The experimental results show that our formal query building approach outperforms existing methods on complex questions while staying competitive on simple questions.