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| Main Authors: | , , |
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| Format: | Preprint |
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2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2302.12239 |
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| _version_ | 1866915095214292992 |
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| author | Galke, Lukas Ram, Yoav Raviv, Limor |
| author_facet | Galke, Lukas Ram, Yoav Raviv, Limor |
| contents | Deep neural networks drive the success of natural language processing. A fundamental property of language is its compositional structure, allowing humans to systematically produce forms for new meanings. For humans, languages with more compositional and transparent structures are typically easier to learn than those with opaque and irregular structures. However, this learnability advantage has not yet been shown for deep neural networks, limiting their use as models for human language learning. Here, we directly test how neural networks compare to humans in learning and generalizing different languages that vary in their degree of compositional structure. We evaluate the memorization and generalization capabilities of a large language model and recurrent neural networks, and show that both deep neural networks exhibit a learnability advantage for more structured linguistic input: neural networks exposed to more compositional languages show more systematic generalization, greater agreement between different agents, and greater similarity to human learners. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_12239 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | What makes a language easy to deep-learn? Deep neural networks and humans similarly benefit from compositional structure Galke, Lukas Ram, Yoav Raviv, Limor Computation and Language I.2.7 Deep neural networks drive the success of natural language processing. A fundamental property of language is its compositional structure, allowing humans to systematically produce forms for new meanings. For humans, languages with more compositional and transparent structures are typically easier to learn than those with opaque and irregular structures. However, this learnability advantage has not yet been shown for deep neural networks, limiting their use as models for human language learning. Here, we directly test how neural networks compare to humans in learning and generalizing different languages that vary in their degree of compositional structure. We evaluate the memorization and generalization capabilities of a large language model and recurrent neural networks, and show that both deep neural networks exhibit a learnability advantage for more structured linguistic input: neural networks exposed to more compositional languages show more systematic generalization, greater agreement between different agents, and greater similarity to human learners. |
| title | What makes a language easy to deep-learn? Deep neural networks and humans similarly benefit from compositional structure |
| topic | Computation and Language I.2.7 |
| url | https://arxiv.org/abs/2302.12239 |