@inproceedings{shi-etal-2022-revisit,
title = "Revisit Systematic Generalization via Meaningful Learning",
author = "Shi, Ning and
Wang, Boxin and
Wang, Wei and
Liu, Xiangyu and
Lin, Zhouhan",
editor = "Bastings, Jasmijn and
Belinkov, Yonatan and
Elazar, Yanai and
Hupkes, Dieuwke and
Saphra, Naomi and
Wiegreffe, Sarah",
booktitle = "Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.blackboxnlp-1.6",
doi = "10.18653/v1/2022.blackboxnlp-1.6",
pages = "62--79",
abstract = "Humans can systematically generalize to novel compositions of existing concepts. Recent studies argue that neural networks appear inherently ineffective in such cognitive capacity, leading to a pessimistic view and a lack of attention to optimistic results. We revisit this controversial topic from the perspective of meaningful learning, an exceptional capability of humans to learn novel concepts by connecting them with known ones. We reassess the compositional skills of sequence-to-sequence models conditioned on the semantic links between new and old concepts. Our observations suggest that models can successfully one-shot generalize to novel concepts and compositions through semantic linking, either inductively or deductively. We demonstrate that prior knowledge plays a key role as well. In addition to synthetic tests, we further conduct proof-of-concept experiments in machine translation and semantic parsing, showing the benefits of meaningful learning in applications. We hope our positive findings will encourage excavating modern neural networks{'} potential in systematic generalization through more advanced learning schemes.",
}
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<abstract>Humans can systematically generalize to novel compositions of existing concepts. Recent studies argue that neural networks appear inherently ineffective in such cognitive capacity, leading to a pessimistic view and a lack of attention to optimistic results. We revisit this controversial topic from the perspective of meaningful learning, an exceptional capability of humans to learn novel concepts by connecting them with known ones. We reassess the compositional skills of sequence-to-sequence models conditioned on the semantic links between new and old concepts. Our observations suggest that models can successfully one-shot generalize to novel concepts and compositions through semantic linking, either inductively or deductively. We demonstrate that prior knowledge plays a key role as well. In addition to synthetic tests, we further conduct proof-of-concept experiments in machine translation and semantic parsing, showing the benefits of meaningful learning in applications. We hope our positive findings will encourage excavating modern neural networks’ potential in systematic generalization through more advanced learning schemes.</abstract>
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%0 Conference Proceedings
%T Revisit Systematic Generalization via Meaningful Learning
%A Shi, Ning
%A Wang, Boxin
%A Wang, Wei
%A Liu, Xiangyu
%A Lin, Zhouhan
%Y Bastings, Jasmijn
%Y Belinkov, Yonatan
%Y Elazar, Yanai
%Y Hupkes, Dieuwke
%Y Saphra, Naomi
%Y Wiegreffe, Sarah
%S Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F shi-etal-2022-revisit
%X Humans can systematically generalize to novel compositions of existing concepts. Recent studies argue that neural networks appear inherently ineffective in such cognitive capacity, leading to a pessimistic view and a lack of attention to optimistic results. We revisit this controversial topic from the perspective of meaningful learning, an exceptional capability of humans to learn novel concepts by connecting them with known ones. We reassess the compositional skills of sequence-to-sequence models conditioned on the semantic links between new and old concepts. Our observations suggest that models can successfully one-shot generalize to novel concepts and compositions through semantic linking, either inductively or deductively. We demonstrate that prior knowledge plays a key role as well. In addition to synthetic tests, we further conduct proof-of-concept experiments in machine translation and semantic parsing, showing the benefits of meaningful learning in applications. We hope our positive findings will encourage excavating modern neural networks’ potential in systematic generalization through more advanced learning schemes.
%R 10.18653/v1/2022.blackboxnlp-1.6
%U https://aclanthology.org/2022.blackboxnlp-1.6
%U https://doi.org/10.18653/v1/2022.blackboxnlp-1.6
%P 62-79
Markdown (Informal)
[Revisit Systematic Generalization via Meaningful Learning](https://aclanthology.org/2022.blackboxnlp-1.6) (Shi et al., BlackboxNLP 2022)
ACL
- Ning Shi, Boxin Wang, Wei Wang, Xiangyu Liu, and Zhouhan Lin. 2022. Revisit Systematic Generalization via Meaningful Learning. In Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, pages 62–79, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.