Communicating artificial neural networks develop efficient color-naming systems. Chaabouni, R., Kharitonov, E., Dupoux, E., & Baroni, M. Proceedings of the National Academy of Sciences, 118(12):e2016569118, March, 2021. ![Communicating artificial neural networks develop efficient color-naming systems [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Significance Color names in human languages are organized into efficient systems optimizing an accuracy/complexity trade-off. We show that artificial neural networks trained with generic deep-learning methods to play a color-discrimination game develop color-naming systems whose distribution on the accuracy/complexity plane is strikingly similar to that of human languages. We proceed to show that efficiency and narrow complexity crucially depend on the discrete nature of communication, acting as an information bottleneck on the emergent code. This suggests that efficient categorization of colors (and possibly other semantic domains) in natural languages does not depend on specific biological constraints of humans, but it is instead a general property of discrete communication systems. , Words categorize the semantic fields they refer to in ways that maximize communication accuracy while minimizing complexity. Focusing on the well-studied color domain, we show that artificial neural networks trained with deep-learning techniques to play a discrimination game develop communication systems whose distribution on the accuracy/complexity plane closely matches that of human languages. The observed variation among emergent color-naming systems is explained by different degrees of discriminative need, of the sort that might also characterize different human communities. Like human languages, emergent systems show a preference for relatively low-complexity solutions, even at the cost of imperfect communication. We demonstrate next that the nature of the emergent systems crucially depends on communication being discrete (as is human word usage). When continuous message passing is allowed, emergent systems become more complex and eventually less efficient. Our study suggests that efficient semantic categorization is a general property of discrete communication systems, not limited to human language. It suggests moreover that it is exactly the discrete nature of such systems that, acting as a bottleneck, pushes them toward low complexity and optimal efficiency.
@article{chaabouni_communicating_2021,
title = {Communicating artificial neural networks develop efficient color-naming systems},
volume = {118},
issn = {0027-8424, 1091-6490},
url = {https://pnas.org/doi/full/10.1073/pnas.2016569118},
doi = {10/gjjr55},
abstract = {Significance
Color names in human languages are organized into efficient systems optimizing an accuracy/complexity trade-off. We show that artificial neural networks trained with generic deep-learning methods to play a color-discrimination game develop color-naming systems whose distribution on the accuracy/complexity plane is strikingly similar to that of human languages. We proceed to show that efficiency and narrow complexity crucially depend on the discrete nature of communication, acting as an information bottleneck on the emergent code. This suggests that efficient categorization of colors (and possibly other semantic domains) in natural languages does not depend on specific biological constraints of humans, but it is instead a general property of discrete communication systems.
,
Words categorize the semantic fields they refer to in ways that maximize communication accuracy while minimizing complexity. Focusing on the well-studied color domain, we show that artificial neural networks trained with deep-learning techniques to play a discrimination game develop communication systems whose distribution on the accuracy/complexity plane closely matches that of human languages. The observed variation among emergent color-naming systems is explained by different degrees of discriminative need, of the sort that might also characterize different human communities. Like human languages, emergent systems show a preference for relatively low-complexity solutions, even at the cost of imperfect communication. We demonstrate next that the nature of the emergent systems crucially depends on communication being discrete (as is human word usage). When continuous message passing is allowed, emergent systems become more complex and eventually less efficient. Our study suggests that efficient semantic categorization is a general property of discrete communication systems, not limited to human language. It suggests moreover that it is exactly the discrete nature of such systems that, acting as a bottleneck, pushes them toward low complexity and optimal efficiency.},
language = {en},
number = {12},
urldate = {2023-01-03},
journal = {Proceedings of the National Academy of Sciences},
author = {Chaabouni, Rahma and Kharitonov, Eugene and Dupoux, Emmanuel and Baroni, Marco},
month = mar,
year = {2021},
pages = {e2016569118},
}
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We proceed to show that efficiency and narrow complexity crucially depend on the discrete nature of communication, acting as an information bottleneck on the emergent code. This suggests that efficient categorization of colors (and possibly other semantic domains) in natural languages does not depend on specific biological constraints of humans, but it is instead a general property of discrete communication systems. , Words categorize the semantic fields they refer to in ways that maximize communication accuracy while minimizing complexity. Focusing on the well-studied color domain, we show that artificial neural networks trained with deep-learning techniques to play a discrimination game develop communication systems whose distribution on the accuracy/complexity plane closely matches that of human languages. The observed variation among emergent color-naming systems is explained by different degrees of discriminative need, of the sort that might also characterize different human communities. Like human languages, emergent systems show a preference for relatively low-complexity solutions, even at the cost of imperfect communication. We demonstrate next that the nature of the emergent systems crucially depends on communication being discrete (as is human word usage). When continuous message passing is allowed, emergent systems become more complex and eventually less efficient. Our study suggests that efficient semantic categorization is a general property of discrete communication systems, not limited to human language. It suggests moreover that it is exactly the discrete nature of such systems that, acting as a bottleneck, pushes them toward low complexity and optimal efficiency.","language":"en","number":"12","urldate":"2023-01-03","journal":"Proceedings of the National Academy of Sciences","author":[{"propositions":[],"lastnames":["Chaabouni"],"firstnames":["Rahma"],"suffixes":[]},{"propositions":[],"lastnames":["Kharitonov"],"firstnames":["Eugene"],"suffixes":[]},{"propositions":[],"lastnames":["Dupoux"],"firstnames":["Emmanuel"],"suffixes":[]},{"propositions":[],"lastnames":["Baroni"],"firstnames":["Marco"],"suffixes":[]}],"month":"March","year":"2021","pages":"e2016569118","bibtex":"@article{chaabouni_communicating_2021,\n\ttitle = {Communicating artificial neural networks develop efficient color-naming systems},\n\tvolume = {118},\n\tissn = {0027-8424, 1091-6490},\n\turl = {https://pnas.org/doi/full/10.1073/pnas.2016569118},\n\tdoi = {10/gjjr55},\n\tabstract = {Significance\n Color names in human languages are organized into efficient systems optimizing an accuracy/complexity trade-off. We show that artificial neural networks trained with generic deep-learning methods to play a color-discrimination game develop color-naming systems whose distribution on the accuracy/complexity plane is strikingly similar to that of human languages. We proceed to show that efficiency and narrow complexity crucially depend on the discrete nature of communication, acting as an information bottleneck on the emergent code. This suggests that efficient categorization of colors (and possibly other semantic domains) in natural languages does not depend on specific biological constraints of humans, but it is instead a general property of discrete communication systems.\n , \n Words categorize the semantic fields they refer to in ways that maximize communication accuracy while minimizing complexity. 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