Neural Enquirer: Learning to Query Tables in Natural Language

Pengcheng Yin, Zhengdong Lu, Hang Li, Ben Kao

Abstract: We propose Neural Enquirer — a neural network architecture for answering natural language (NL) questions given a knowledge base (KB) table. Unlike previous work on end-to-end training of semantic parsers, Neural Enquirer is fully “neuralized”: it gives distributed representations of queries and KB tables, and executes queries through a series of differentiable operations. The model can be trained with gradient descent using both end-to-end and step-by-step supervision. During training the representations of queries and the KB table are jointly optimized with the query execution logic. Our experiments show that the model can learn to execute complex NL queries on KB tables with rich structures.

Conflicts: cs.hku.hk, huawei.com