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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 9: AAAI-21 Technical Tracks 9

Neural Sequence-to-grid Module for Learning Symbolic Rules

February 1, 2023

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Authors

Segwang Kim

Seoul National University


Hyoungwook Nam

University of Illinois at Urbana-Champaign


Joonyoung Kim

Seoul National University


Kyomin Jung

Seoul National University


DOI:

10.1609/aaai.v35i9.16994


Abstract:

Logical reasoning tasks over symbols, such as learning arithmetic operations and computer program evaluations, have become challenges to deep learning. In particular, even state-of-the-art neural networks fail to achieve textit{out-of-distribution} (OOD) generalization of symbolic reasoning tasks, whereas humans can easily extend learned symbolic rules. To resolve this difficulty, we propose a neural sequence-to-grid (seq2grid) module, an input preprocessor that automatically segments and aligns an input sequence into a grid. As our module outputs a grid via a novel differentiable mapping, any neural network structure taking a grid input, such as ResNet or TextCNN, can be jointly trained with our module in an end-to-end fashion. Extensive experiments show that neural networks having our module as an input preprocessor achieve OOD generalization on various arithmetic and algorithmic problems including number sequence prediction problems, algebraic word problems, and computer program evaluation problems while other state-of-the-art sequence transduction models cannot. Moreover, we verify that our module enhances TextCNN to solve the bAbI QA tasks without external memory.

Topics: AAAI

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HOW TO CITE:

Segwang Kim||Hyoungwook Nam||Joonyoung Kim||Kyomin Jung Neural Sequence-to-grid Module for Learning Symbolic Rules Proceedings of the AAAI Conference on Artificial Intelligence (2021) 8163-8171.

Segwang Kim||Hyoungwook Nam||Joonyoung Kim||Kyomin Jung Neural Sequence-to-grid Module for Learning Symbolic Rules AAAI 2021, 8163-8171.

Segwang Kim||Hyoungwook Nam||Joonyoung Kim||Kyomin Jung (2021). Neural Sequence-to-grid Module for Learning Symbolic Rules. Proceedings of the AAAI Conference on Artificial Intelligence, 8163-8171.

Segwang Kim||Hyoungwook Nam||Joonyoung Kim||Kyomin Jung. Neural Sequence-to-grid Module for Learning Symbolic Rules. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.8163-8171.

Segwang Kim||Hyoungwook Nam||Joonyoung Kim||Kyomin Jung. 2021. Neural Sequence-to-grid Module for Learning Symbolic Rules. "Proceedings of the AAAI Conference on Artificial Intelligence". 8163-8171.

Segwang Kim||Hyoungwook Nam||Joonyoung Kim||Kyomin Jung. (2021) "Neural Sequence-to-grid Module for Learning Symbolic Rules", Proceedings of the AAAI Conference on Artificial Intelligence, p.8163-8171

Segwang Kim||Hyoungwook Nam||Joonyoung Kim||Kyomin Jung, "Neural Sequence-to-grid Module for Learning Symbolic Rules", AAAI, p.8163-8171, 2021.

Segwang Kim||Hyoungwook Nam||Joonyoung Kim||Kyomin Jung. "Neural Sequence-to-grid Module for Learning Symbolic Rules". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.8163-8171.

Segwang Kim||Hyoungwook Nam||Joonyoung Kim||Kyomin Jung. "Neural Sequence-to-grid Module for Learning Symbolic Rules". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 8163-8171.

Segwang Kim||Hyoungwook Nam||Joonyoung Kim||Kyomin Jung. Neural Sequence-to-grid Module for Learning Symbolic Rules. AAAI[Internet]. 2021[cited 2023]; 8163-8171.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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