How agents see things: On visual representations in an emergent language game

Abstract

There is growing interest in the language developed by agents interacting in emergentcommunication settings. Earlier studies have focused on the agents' symbol usage, rather than on their representation of visual input. In this paper, we consider the referential games of Lazaridou et al. (2017), and investigate the representations the agents develop during their evolving interaction. We find that the agents establish successful communication by inducing visual representations that almost perfectly align with each other, but, surprisingly, do not capture the conceptual properties of the objects depicted in the input images. We conclude that, if we are interested in developing language-like communication systems, we must pay more attention to the visual semantics agents associate to the symbols they use.

Analyzing Redundancy in Pretrained Transformer Models

“…Moreover, cka is known to outperform other methods such as CCA and SVCCA (Raghu et al, 2017), in identifying relationships between different layers across different architectures. While there are several other methods proposed in literature to analyze and compare representations (Kriegeskorte et al, 2008;Bouchacourt and Baroni, 2018;Chrupała and Alishahi, 2019;Chrupała, 2019), we do not intend to compare them here and instead use cka to show redundancy in the network. The mathematical definition of cka is provided in Appendix A.6 for the reader.…”

The Grammar of Emergent Languages

“…Previous work that focused on the analysis of emergent languages has primarily concentrated on semantics-based analysis. In particular, they considered whether agents transmit information about categories or objects, or instead communicate using low-level feature information (Steels, 2010;Lazaridou et al, 2017;Bouchacourt and Baroni, 2018;Mihai and Hare, 2019, i.a. ).…”

“…For instance, and Havrylov and Titov (2017) showed that emergent languages can encode category-specific information through prefixing as well as wordorder and hierarchical coding, respectively. Others instead have used qualitative inspection to support the claim that messages focus on pixel information instead of concepts (Bouchacourt and Baroni, 2018), that agents consistently use certain words for specific situations (Mul et al, 2019) or re-use the same words for different property values (Lu et al, 2020), or that languages represent distinct properties of the objects (e.g. colour and shape) under specific circumstances (Kottur et al, 2017;Choi et al, 2018;Słowik et al, 2020).…”

“…Another popular approach to analyse the semantics of emergent languages relies on representational similarity analysis (RSA, Kriegeskorte et al, 2008). RSA is used to analyse the similarity between the language space and the meaning space, in which case it is also called topographic simi-larity (Brighton et al, 2005;Brighton and Kirby, 2006;Andreas, 2019;Li and Bowling, 2019;Keresztury and Bruni, 2020;Słowik et al, 2020;Ren et al, 2020), It has also been used to directly compare the continuous hidden representations of a neural agent with the input space (Bouchacourt and Baroni, 2018).…”

Miss Tools and Mr Fruit: Emergent Communication in Agents Learning about Object Affordances

“…For such inquiries to be meaningful, the designed setup should reflect as many aspects of human communication as possible. Moreover, appropriate tools should be applied to the analysis of emergent communication, since, as several recent studies have shown, agents might succeed at a task without truly relying on their communicative channel, or by means of ad-hoc communication techniques overfitting their environment (Kottur et al, 2017;Bouchacourt and Baroni, 2018;Lowe et al, 2019).…”