Weakly Supervised Neural Symbolic Learning for Cognitive Tasks

Authors

  • Jidong Tian Shanghai Jiao Tong University
  • Yitian Li Shanghai Jiao Tong University
  • Wenqing Chen Shanghai Jiao Tong University
  • Liqiang Xiao Shanghai Jiao Tong University
  • Hao He Shanghai Jiao Tong University
  • Yaohui Jin Shanghai Jiao Tong University

DOI:

https://doi.org/10.1609/aaai.v36i5.20533

Keywords:

Knowledge Representation And Reasoning (KRR)

Abstract

Despite the recent success of end-to-end deep neural networks, there are growing concerns about their lack of logical reasoning abilities, especially on cognitive tasks with perception and reasoning processes. A solution is the neural symbolic learning (NeSyL) method that can effectively utilize pre-defined logic rules to constrain the neural architecture making it perform better on cognitive tasks. However, it is challenging to apply NeSyL to these cognitive tasks because of the lack of supervision, the non-differentiable manner of the symbolic system, and the difficulty to probabilistically constrain the neural network. In this paper, we propose WS-NeSyL, a weakly supervised neural symbolic learning model for cognitive tasks with logical reasoning. First, WS-NeSyL employs a novel back search algorithm to sample the possible reasoning process through logic rules. This sampled process can supervise the neural network as the pseudo label. Based on this algorithm, we can backpropagate gradients to the neural network of WS-NeSyL in a weakly supervised manner. Second, we introduce a probabilistic logic regularization into WS-NeSyL to help the neural network learn probabilistic logic. To evaluate WS-NeSyL, we have conducted experiments on three cognitive datasets, including temporal reasoning, handwritten formula recognition, and relational reasoning datasets. Experimental results show that WS-NeSyL not only outperforms the end-to-end neural model but also beats the state-of-the-art neural symbolic learning models.

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Published

2022-06-28

How to Cite

Tian, J., Li, Y., Chen, W., Xiao, L., He, H., & Jin, Y. (2022). Weakly Supervised Neural Symbolic Learning for Cognitive Tasks. Proceedings of the AAAI Conference on Artificial Intelligence, 36(5), 5888-5896. https://doi.org/10.1609/aaai.v36i5.20533

Issue

Section

AAAI Technical Track on Knowledge Representation and Reasoning