On the role of population heterogeneity in emergent communication

1 INTRODUCTION . Language emergence has been explored in linguistics and artificial intelligence for two main reasons ( Lazaridou & Baroni , 2020 ) . On the one hand , artificially reproducing language emergence may help to understand the evolution of human languages ( Steels , 1997 ; Briscoe , 2002 ; Wagner et al. , 2003 ) . On the other hand , language is known to be structured , and compositional ( Bickerton , 2007 ) , and imitating such properties would enhance machine learning representations . As a result , there exists a constant back and forth between cognitive sciences , linguistics , and artificial intelligence to retrieve the core ingredients of language emergence ( Kirby et al. , 2008 ) . In this paper , we explore how the size of a population may impact the structure of emerging languages by using neural reinforcement learning methods . Especially , we explore the following socio-linguistic hypothesis : larger communities create more systematic languages ( Raviv et al. , 2019a ; 2020 ) . This hypothesis has been supported by a number of ethnographic ( Gary Lupyan , 2010 ) and socio-linguistics ( Raviv et al. , 2020 ) observations as well as behavioral studies mimicking language emergence in a controlled setup ( Raviv et al. , 2019a ) . Few neural language emergence papers have explored how community size impacts language structure so far , but the available evidence is mitigated at best . Tieleman et al . ( 2019 ) observed a small but consistent regularization effect when pairing auto-encoders within a population . Similarly , Cogswell et al . ( 2019 ) observed slight improvements in language compositionality with a large population but only reported them in few experimental settings . Finally , Graesser et al . ( 2019 ) studied the impact of contact-agents for different population sizes , but they did not observe a correlation between the population size and the convergence speed , success rate , or mutual agent intelligibility . The following question arises : why does community size not improve language properties in recent emergent communication literature , although it is a key structuring factor in broader linguistics literature ? We argue that recent emergent communication models are limited as they ignore individual learning capacities by working only with homogeneous populations . Consequently , they miss coupling effects emerging from agents ’ asymmetries . As a result , we hypothesize that community size effects could occur as soon as local heterogeneities are introduced into populations . In this work , we explore the effects of population size with neural agents in the well-known Lewis referential game ( Lewis , 1969 ) . In this game , a speaker describes a hidden object to a listener , which must then reconstruct object properties . Both agents thus need to co-develop a communication protocol to solve the task . The population-based variant of this game randomly pairs one speaker and one listener from their respective communities . The goal is to observe whether increasing the number of agents enhances the communication protocol qualities , e.g . success rate , compositionality , generalization etc . ( Kottur et al. , 2017 ; Chaabouni et al. , 2020 ; Lazaridou et al. , 2018 ) . Firstly , we reproduce Lewis reconstruction setting and confirm the experimental difference : when increasing the number of agents , we do not observe improvements over various emergent language metrics . We thus question the current paradigm to model population in the language emergence literature . In particular , all agents are trained uniformly , i.e. , their learning speed , capacity , sampling are identical ( Tieleman et al. , 2019 ; Cogswell et al. , 2019 ; Fitzgerald , 2019 ) . Secondly , we evaluate the impact of two potential sources of model heterogeneities : ( i ) agents learning speed , ( ii ) agents capacities . In both cases , we observe a similar pattern : the absolute value of speakerlistener speed and capacity is not important , yet their relative value is crucial . We hence shed light on the strong correlation between language structures and agents relative training facilities . Thirdly , we push this reasoning further by distributing learning speeds across the population thus creating heterogeneous populations . We there observe an improvement of language scores combined with a variance reduction when increasing population sizes . In other words , larger communities of neural agents start developing more stable and structured languages when being heterogeneously designed . This observation brings a first stone toward solving the empirical and computational contradiction . Our experiments partially removed the apparent contradiction between the socio-linguistic observations and the recent emergent communication literature . They illustrate how crucial population training dynamics are in shaping emergent languages and how population heterogeneity may have been underestimated in the recent emergent communication literature . All in all , our contributions are three-fold : ( i ) we empirically show that the community size is not a structuring factor in language emergence by or in itself in the classic homogeneous Lewis setting ; ( ii ) we give evidences that speaker-listener relative dynamics strongly affects language properties ; ( iii ) we provide the first computational cues to remove the apparent difference between socio-linguistic literature and recent neural emergent communication works . 2 RELATED WORK . Population size in sociolinguistics . Population size is a core parameter defining the social environment an agent interacts with . Its impact on language structures has largely been studied on humans ( Nettle , 2012 ; Bromham et al. , 2015 ; Reali et al. , 2018 ; Raviv et al. , 2019a ) and animals ( Blumstein & Armitage , 1997 ; McComb & Semple , 2005 ; Wilkinson , 2013 ) . By analyzing 2000 languages ( Dryer & Haspelmath , 2013 ) , a clear correlation was drawn between population size and diverse language features , e.g . larger communities tend to develop simpler grammars ( Gary Lupyan , 2010 ; Meir et al. , 2012 ; Reali et al. , 2018 ) . As part of their research on the influence of network structures on language emergence ( Raviv et al. , 2019a ; b ; 2020 ) , Raviv et al . ( 2019a ) went one step further by arguing that the community size is predictive of language structure and diversity . To do so , they split 150 people into different groups of given community size to isolate confounding factors . While people played a speaker-listener Lewis game , they observe that the greater the community size , the simpler and more consistent the generated language . Here , we intend to test this assumption in the context of neural language emergence . All things considered , we adopt a setting close to Raviv et al . ( 2019a ) ’ s when computationally modeling human population . Populations in experimental emergent communication . Experimental language emergence has mainly been studied with two methods : behavioral studies ( Kegl , 1994 ; Sandler et al. , 2005 ) and simulations ( neural and non neural ) ( Wagner et al. , 2003 ; Lazaridou & Baroni , 2020 ) . From behavioral studies and non neural simulations , two main approaches have emerged in the past 20 years : experimental semiotics ( Galantucci & Garrod , 2011 ; Garrod et al. , 2007 ) and iterated learning ( Kirby & Hurford , 2002 ; Kirby et al. , 2008 ; Beckner et al. , 2017 ) . According to experimental semiotics studies , languages are mainly subject to an expressivity pressure ; they argue that messages should be highly informative to allow communication within a group ( Fay & Ellison , 2013 ) . According to Iterated Learning paradigm , structures emerge from a compressibility pressure ; they argue that memory limitations compel messages to become simpler to be easily learned ( Tamariz & Kirby , 2015 ) , which is also referred to as transmission bottleneck ( Smith et al. , 2003 ) . Kirby et al . ( 2015 ) then combined those two approaches and show that languages emerge as a trade-off between expressivity and compressivity during cultural evolution . Recently , similar ideas have been modeled in emergent communication frameworks involving neural agents ( Ren et al. , 2020 ; Lu et al. , 2020 ) . Because seminal non-neural simulations used specific optimization methods and models across studies ( Wagner et al. , 2003 ) , it is hard to generalize a global picture . Indeed , Wagner et al . ( 2003 ) keep listing standalone experiments . Modern neural agents manage to simplify and standardize agents ’ modeling . It thus enables explore the determinants of different structural properties of human languages within a unique framework , paving the way for holistic models of emergence communication ( Lazaridou & Baroni , 2020 ) . Our paper is related to this last set of neural works . Recent works in emergent communication have been debating the prerequisites to the emergence of language universals such as compositionality ( Li & Bowling , 2019 ; Ren et al. , 2020 ; Lazaridou et al. , 2018 ; Mordatch & Abbeel , 2018 ; Resnick et al. , 2020 ; Kottur et al. , 2017 ; Choi et al. , 2018 ; Łukasz Kuciński et al. , 2020 ) , generalisation ( Baroni , 2020 ; Chaabouni et al. , 2020 ; Hupkes et al. , 2020 ; Denamganaı̈ & Walker , 2020 ) , efficiency ( Chaabouni et al. , 2019 ; Rita et al. , 2020 ) or stability ( Kharitonov et al. , 2020 ; Lu et al. , 2020 ) . Among them , a few works explored how different population structures may impact properties of emergent languages . Inspired by iterated learning methods ( Kirby et al. , 2008 ) , Ren et al . ( 2020 ) ; Li & Bowling ( 2019 ) ; Lu et al . ( 2020 ) look at language evolution across multiple generations of agent pairs , i.e . population is spread over time . However , we consider here a population where multiple agent-pairs coexist simultaneously within a single generation . There , Graesser et al . ( 2019 ) show that community of agents start coordinating their language when at least three agents are present in the community . They later assume that increasing the community size may impact the emergent shared language , but did not observe it in their initial experiments . A similar hypothesis was also made by Bouchacourt & Baroni ( 2019 ) while analyzing the influence of symmetric agents in the emergence of a common language . With different research objectives , Cogswell et al . ( 2019 ) exhibit a slight compositionality enhancement when increasing population size without the need of language transmission . Analogously , Tieleman et al . ( 2019 ) explicitly study community size and display a small but consistent gain of abstraction and structure within speakers ’ latent representations by increasing population size . Eventually , Fitzgerald ( 2019 ) suggest that populations improve regularization and generalization compared to single speaker-listener pairs but underlines that there is not a clear correlation between community size and learning properties . In this paper , we aim at analyzing how population size affects those discussed properties . We align with emergent communication literature and show that naively increasing community size does not consistently improve language properties . We then challenge the homogeneity assumption made in all population designs . 3 METHOD . We here describe the different components of language emergence in a population-based Lewis Game , namely , game rules , notations , training dynamics , and evaluation metrics . Finally , we define how we alter population dynamics by asymmetrizing agents and injecting heterogeneities .

Summary: The authors refer to prior work in sociolinguistic literature to state that larger communities create more systematic languages. However, they point out, this apparent correlation between language structure and population size has evaded machine learning practitioners studying language emergence. This paper claims that populations explored in machine learning have largely been homogeneous and that population heterogeneity is key to the emergence of structure in artificial agents. The authors reproduce the failure to achieve systematic languages by scaling up the population and then show that they can indeed get more structure by introducing heterogeneity. They explore introducing asymmetry between the speaker and the listener based on model capacity and learning speed, leading to an increase in language structure when the speaker is faster or has more capacity. The authors note that this effect only depends on the relative differences between the speaker and the listener and not the absolute values (there is a correlation with absolute values but the variation has a low magnitude). Further, they show that larger networks need fewer epochs to reach similar training accuracy, concluding that network capacity is a confounding factor of training speed in their setting. Finally, the authors create a population of heterogeneous agents by imbibing them with different learning speeds by updating an agent $i$ with probability $p_i$ after each round of the Lewis game. Four properties of the emergent language are studied: speakers synchronization, (negative) conditional entropy given an object, topographic similarity, and generalization. These metrics either improve or remain approximately at the same values as the population size increases.