Year: 2020
Type: preprint
Abstract: Compositional generalization is a basic and essential intellective capability of human beings, which allows us to recombine known parts readily. However, existing neural network based models have been proven to be extremely deficient in such a capability. Inspired by work in cognition which argues compositionality can be captured by variable slots with symbolic functions, we present a refreshing view that connects a memory-augmented neural model with analytical expressions, to achieve compositional generalization. Our model consists of two cooperative neural modules, Composer and Solver, fitting well with the cognitive argument while being able to be trained in an end-to-end manner via a hierarchical reinforcement learning algorithm. Experiments on the well-known benchmark SCAN demonstrate that our model seizes a great ability of compositional generalization, solving all challenges addressed by previous works with 100% accuracies.
Source: arXiv (Cornell University)
Authors: Liu, Qian, An, Shengnan, Lou, Jian-Guang, Chen, Bei, Lin, Zeqi, Gao, Yan, Zhou, Bin, Zheng, Nanning, Zhang, Dongmei
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G5158269727, G7953880188
OpenAlex ID: W3036175207