Natural Language Business Intelligence Question Answering Through SeqtoSeq Transfer Learning

Abstract

Enterprise data is usually stored in the form of relational databases. Question Answering systems provides an easier way so that business analysts can get data insights without struggling with the syntax of SQL. However, building a supervised machine learning based question answering system is a challenging task involving large manual annotations for a specific domain. In this paper we explore the problem of transfer learning for neural sequence taggers, where a source task with plentiful annotations (e.g., Training samples (NL questions) on IT enetrprize domain) is used to improve performance on a target task with fewer available annotations (e.g., Training samples (NL questions) on pharmaceutical domain). We examine the effects of transfer learning for deep recurrent networks across domains and show that significant improvement can often be obtained. Our question answering framework is based on a set of machine learning models that create an intermediate sketch from a natural language query. Using the intermediate sketch, we generate a final database query over a large knowledge graph. Our framework supports multiple queries such as aggregation, self joins, factoid and transnational.