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Poster
ProTo: Program-Guided Transformer for Program-Guided Tasks
Zelin Zhao · Karan Samel · Binghong Chen · lee song

Thu Dec 09 04:30 PM -- 06:00 PM (PST) @ None #None
in Poster Session 7 »

Programs, consisting of semantic and structural information, play an important role in the communication between humans and agents. Towards learning general program executors to unify perception, reasoning, and decision making, we formulate program-guided tasks which require learning to execute a given program on the observed task specification. Furthermore, we propose Program-Guided Transformers (ProTo), which integrates both semantic and structural guidance of a program by leveraging cross-attention and masked self-attention to pass messages between the specification and routines in the program. ProTo executes a program in a learned latent space and enjoys stronger representation ability than previous neural-symbolic approaches. We demonstrate that ProTo significantly outperforms the previous state-of-the-art methods on GQA visual reasoning and 2D Minecraft policy learning datasets. Additionally, ProTo demonstrates better generalization to unseen, complex, and human-written programs.

Keywords: Transformers

Author Information

Zelin Zhao (The Chinese University of Hong Kong)
Karan Samel (Georgia Institute of Technology)
Binghong Chen (Georgia Institute of Technology)
lee song (Georgia Institute of Technology)

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