Coupling Distributed and Symbolic Execution for Natural Language Queries

Lili Mou, Zhengdong Lu, Hang Li, Zhi Jin

TL;DR: ICLR 2017 workshop submission

Abstract: In this paper, we propose to combine neural execution and symbolic execution to query a table with natural languages. Our approach makes use the differentiability of neural networks and transfers (imperfect) knowledge to the symbolic executor before reinforcement learning. Experiments show our approach achieves high learning efficiency, high execution efficiency, high interpretability, as well as high performance.

Conflicts: pku.edu.cn, huawei.com, DeeplyCurious.ai