Internal and external pressures on language emergence: least effort, object constancy and frequency

Diana Rodríguez Luna, Edoardo Maria Ponti, Dieuwke Hupkes, Elia Bruni


Abstract
In previous work, artificial agents were shown to achieve almost perfect accuracy in referential games where they have to communicate to identify images. Nevertheless, the resulting communication protocols rarely display salient features of natural languages, such as compositionality. In this paper, we propose some realistic sources of pressure on communication that avert this outcome. More specifically, we formalise the principle of least effort through an auxiliary objective. Moreover, we explore several game variants, inspired by the principle of object constancy, in which we alter the frequency, position, and luminosity of the objects in the images. We perform an extensive analysis on their effect through compositionality metrics, diagnostic classifiers, and zero-shot evaluation. Our findings reveal that the proposed sources of pressure result in emerging languages with less redundancy, more focus on high-level conceptual information, and better abilities of generalisation. Overall, our contributions reduce the gap between emergent and natural languages.
Anthology ID:
2020.findings-emnlp.397
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4428–4437
Language:
URL:
https://www.aclweb.org/anthology/2020.findings-emnlp.397
DOI:
10.18653/v1/2020.findings-emnlp.397
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.findings-emnlp.397.pdf