Question answering

Linguistics

Philosophy

Psychoanalysis

Knowledge representation and reasoning

Theoretical computer science

Computer science

TRACE (psycholinguistics)

Natural language processing

Psychology

Politics

The Symbolic

Political science

Artificial intelligence

Representation (politics)

Symbolic data analysis

Machine learning

Visual reasoning

arXiv (Cornell University)

We marry two powerful ideas: deep representation learning for visual recognition and language understanding, and symbolic program execution for reasoning. Our neural-symbolic visual question answering (NS-VQA) system first recovers a structural scene representation from the image and a program trace from the question. It then executes the program on the scene representation to obtain an answer. Incorporating symbolic structure as prior knowledge offers three unique advantages. First, executing programs on a symbolic space is more robust to long program traces; our model can solve complex reasoning tasks better, achieving an accuracy of 99.8% on the CLEVR dataset. Second, the model is more data- and memory-efficient: it performs well after learning on a small number of training data; it can also encode an image into a compact representation, requiring less storage than existing methods for offline question answering. Third, symbolic program execution offers full transparency to the reasoning process; we are thus able to interpret and diagnose each execution step.

Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding