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: This paper aims at solving a conflicting empirical observation and the present-state models for emerging languages. It has been observed that larger populations produce more structured languages. However, the state-of-art neural-based models have not been able to generate languages with such characteristics. This paper shows that a key ingredient is to allow population heterogeneity in the neural models rather than the current identically distributed specifications.