@inproceedings{gupta-etal-2020-compositionality,
title = "Compositionality and Capacity in Emergent Languages",
author = "Gupta, Abhinav and
Resnick, Cinjon and
Foerster, Jakob and
Dai, Andrew and
Cho, Kyunghyun",
editor = "Gella, Spandana and
Welbl, Johannes and
Rei, Marek and
Petroni, Fabio and
Lewis, Patrick and
Strubell, Emma and
Seo, Minjoon and
Hajishirzi, Hannaneh",
booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.repl4nlp-1.5",
doi = "10.18653/v1/2020.repl4nlp-1.5",
pages = "34--38",
abstract = "Recent works have discussed the extent to which emergent languages can exhibit properties of natural languages particularly learning compositionality. In this paper, we investigate the learning biases that affect the efficacy and compositionality in multi-agent communication in addition to the communicative bandwidth. Our foremost contribution is to explore how the capacity of a neural network impacts its ability to learn a compositional language. We additionally introduce a set of evaluation metrics with which we analyze the learned languages. Our hypothesis is that there should be a specific range of model capacity and channel bandwidth that induces compositional structure in the resulting language and consequently encourages systematic generalization. While we empirically see evidence for the bottom of this range, we curiously do not find evidence for the top part of the range and believe that this is an open question for the community.",
}
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<abstract>Recent works have discussed the extent to which emergent languages can exhibit properties of natural languages particularly learning compositionality. In this paper, we investigate the learning biases that affect the efficacy and compositionality in multi-agent communication in addition to the communicative bandwidth. Our foremost contribution is to explore how the capacity of a neural network impacts its ability to learn a compositional language. We additionally introduce a set of evaluation metrics with which we analyze the learned languages. Our hypothesis is that there should be a specific range of model capacity and channel bandwidth that induces compositional structure in the resulting language and consequently encourages systematic generalization. While we empirically see evidence for the bottom of this range, we curiously do not find evidence for the top part of the range and believe that this is an open question for the community.</abstract>
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%0 Conference Proceedings
%T Compositionality and Capacity in Emergent Languages
%A Gupta, Abhinav
%A Resnick, Cinjon
%A Foerster, Jakob
%A Dai, Andrew
%A Cho, Kyunghyun
%Y Gella, Spandana
%Y Welbl, Johannes
%Y Rei, Marek
%Y Petroni, Fabio
%Y Lewis, Patrick
%Y Strubell, Emma
%Y Seo, Minjoon
%Y Hajishirzi, Hannaneh
%S Proceedings of the 5th Workshop on Representation Learning for NLP
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F gupta-etal-2020-compositionality
%X Recent works have discussed the extent to which emergent languages can exhibit properties of natural languages particularly learning compositionality. In this paper, we investigate the learning biases that affect the efficacy and compositionality in multi-agent communication in addition to the communicative bandwidth. Our foremost contribution is to explore how the capacity of a neural network impacts its ability to learn a compositional language. We additionally introduce a set of evaluation metrics with which we analyze the learned languages. Our hypothesis is that there should be a specific range of model capacity and channel bandwidth that induces compositional structure in the resulting language and consequently encourages systematic generalization. While we empirically see evidence for the bottom of this range, we curiously do not find evidence for the top part of the range and believe that this is an open question for the community.
%R 10.18653/v1/2020.repl4nlp-1.5
%U https://aclanthology.org/2020.repl4nlp-1.5
%U https://doi.org/10.18653/v1/2020.repl4nlp-1.5
%P 34-38
Markdown (Informal)
[Compositionality and Capacity in Emergent Languages](https://aclanthology.org/2020.repl4nlp-1.5) (Gupta et al., RepL4NLP 2020)
ACL
- Abhinav Gupta, Cinjon Resnick, Jakob Foerster, Andrew Dai, and Kyunghyun Cho. 2020. Compositionality and Capacity in Emergent Languages. In Proceedings of the 5th Workshop on Representation Learning for NLP, pages 34–38, Online. Association for Computational Linguistics.