Question: How to predict query substructures contained in the question?
Evidence: Knowledge-based question answering (KBQA) aims to answer natural language questions over knowledge bases (KBs) such as DBpedia and Freebase. Formal query generation is an important component in many KBQA systems BIBREF0 , BIBREF1 , BIBREF2 , especially for answering complex questions. Given entity and relation linking results, formal query generation aims to generate correct executable queries, e.g., SPARQL queries, for the input natural language questions. An example question and its formal query are shown in Figure FIGREF1 . Generally speaking, formal query generation is expected to include but not be limited to have the capabilities of (i) recognizing and paraphrasing different kinds of constraints, including triple-level constraints (e.g., “movies" corresponds to a typing constraint for the target variable) and higher level constraints (e.g., subgraphs). For instance, “the same ... as" represents a complex structure shown in the middle of Figure FIGREF1 ; (ii) recognizing and paraphrasing aggregations (e.g., “how many" corresponds to Count); and (iii) organizing all the above to generate an executable query BIBREF3 , BIBREF4 .
There are mainly two kinds of query generation approaches for complex questions. (i) Template-based approaches choose a pre-collected template for query generation BIBREF1 , BIBREF5 . Such approaches highly rely on the coverage of templates, and perform unstably when some complex templates have very few natural language questions as training data. (ii) Approaches based on semantic parsing and neural networks learn entire representations for questions with different query structures, by using a neural network following the encode-and-compare framework BIBREF2 , BIBREF4 . They may suffer from the lack of training data, especially for long-tail questions with rarely appeared structures. Furthermore, both above approaches cannot handle questions with unseen query structures, since they cannot generate new query structures.
To cope with the above limitations, we propose a new query generation approach based on the following observation: the query structure for a complex question may rarely appear, but it usually contains some substructures that frequently appeared in other questions. For example, the query structure for the question in Figure FIGREF1 appears rarely, however, both “how many movies" and “the same ... as" are common expressions, which correspond to the two query substructures in dashed boxes. To collect such frequently appeared substructures, we automatically decompose query structures in the training data. Instead of directly modeling the query structure for the given question as a whole, we employ multiple neural networks to predict query substructures contained in the question, each of which delivers a part of the query intention. Then, we select an existing query structure for the input question by using a combinational ranking function. Also, in some cases, no existing query structure is appropriate for the input question. To cope with this issue, we merge query substructures to build new query structures. The contributions of this paper are summarized below:
Instead of directly modeling the query structure for the given question as a whole, we employ multiple neural networks to predict query substructures contained in the question, each of which delivers a part of the query intention.
Answer: The authors employ multiple neural networks to predict query substructures contained in the question.