تقرير
On the Correspondence between Compositionality and Imitation in Emergent Neural Communication
| العنوان: | On the Correspondence between Compositionality and Imitation in Emergent Neural Communication |
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| المؤلفون: | Cheng, Emily, Rita, Mathieu, Poibeau, Thierry |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Neural and Evolutionary Computing |
| الوصف: | Compositionality is a hallmark of human language that not only enables linguistic generalization, but also potentially facilitates acquisition. When simulating language emergence with neural networks, compositionality has been shown to improve communication performance; however, its impact on imitation learning has yet to be investigated. Our work explores the link between compositionality and imitation in a Lewis game played by deep neural agents. Our contributions are twofold: first, we show that the learning algorithm used to imitate is crucial: supervised learning tends to produce more average languages, while reinforcement learning introduces a selection pressure toward more compositional languages. Second, our study reveals that compositional languages are easier to imitate, which may induce the pressure toward compositional languages in RL imitation settings. Comment: Findings of ACL 2023; 5 pages + 8 pages of supplementary materials |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2305.12941 |
| رقم الأكسشن: | edsarx.2305.12941 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |