The emergence of symbolic language has always been an important issue,
The emergence of language has always been an important issue,
which attracts attention from a broad range of communities,
including philology~\cite{}, biology~\cite{}, and computer
science~\cite{}. Especially in computer science, efforts in recent years trying to explore
the emergence of symbolic language in virtual multi-agent environments, where
the emergent language in virtual multi-agent environments, where
agents are trained to communicate with neural-network-based methods such as deep
reinforcement learning~\cite{}.
%Such works can be roughly classified into two categories,
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@@ -13,7 +13,7 @@ reinforcement learning~\cite{}.
%the environment setting.
The quality of emergent symbolic language is typically measured by its
The quality of emergent language is typically measured by its
\emph{compositionality}.
Compositionality is a principle that determines
whether the meaning of a complex expression (e.g, phrase), which is assembled out of a
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@@ -72,14 +72,16 @@ vocabulary can express almost infinite concepts.}
\end{table*}
Prior studies focus on achieving high compositional symbolic language
through \emph{deliberately handcrafted} inductions, e.g., small vocabulary
sizes~\cite{}, memoryless~\cite{}, additional rewards~\cite{}, constructed loss functions~\cite{}, and
ease-of-teaching~\cite{}. \note{Such optimization methodologies are driven by the challenges to generate high compositional symbolic without induction in an existing multi-agent environment.}
through \emph{deliberately handcrafted} inductions, e.g., additional rewards~\cite{},
constructed loss functions~\cite{}, structural input data~\cite{},
memoryless~\cite{}, and ease-of-teaching~\cite{}.
\note{Such optimization methodologies are driven by the challenges to generate high compositional symbolic without induction in an existing multi-agent environment.}
Figure~\ref{fig:induction} reports the compositionality when training two agents
in the widely-used listener-speaker referential game for emerging 100 symbolic
in the widely-used listener-speaker referential game~\cite{} for emerging 100
languages, and it can be observed that \note{the compositionality
of emerged symbolic language is extremely low without any induction. Moreover, varying
of emergent language is seldom high (e.g., $<5\%$ for compositionality $>0.99$)
without any induction. Moreover, varying
the vocabulary size does not affect the compositionality notably.}
Though such unnatural inductions are useful, they prevent us from better understanding the mystery of
the emergence of language and even intelligence among our pre-human ancestors.
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@@ -89,14 +91,14 @@ In other words, it is never clear whether \emph{natural}
environment and agents are sufficient for achieving high compositionality.
This paper is the first one to achieve high compositional
symbolic language without any deliberately handcrafted induction. The key observation
language without any deliberately handcrafted induction. The key observation
is that the internal \emph{agent capacity} plays a crucial role in the
compositionality of symbolic language.
compositionality of emergent language.
%by thoroughly
%analyzing the compositionality after removing the inductions in
%the most widely-used listener-speaker referential game framework.
Concretely, the relationship between the agent capacity and the compositionality
of symbolic language is characterized, with a novel mutual information-based
Concretely, the relationship between the agent capacity and the compositionality
of emergent language is characterized, with a novel mutual information-based
metric for the compositionality.
%both theoretically and experimentally.
%theoretically
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@@ -111,11 +113,11 @@ capacity and the compositionality of symbolic language that emerged
%two different dedicated experiments, i.e., \note{XXX and XXX, are utilized for XXX}.
%Regarding the experimental validation, it is conducted on a listener-speaker
%referential game framework with eliminated unnatural inductions.
Both the theoretical analysis and experimental results lead to a counter-intuitive
conclusion that \emph{lower agent capacity facilitates the emergence of symbolic language
Both the theoretical analysis and experimental results lead to a counter-intuitive
conclusion that \emph{lower agent capacity facilitates the emergence of language
with higher compositionality}. \note{Therefore, by only reducing the agent capacity
in such a natural environment, we
can generate a higher compositional symbolic language with a higher probability.}
in such a natural environment, we
can generate a more compositional language with a higher probability.}
%Prior studies focus on investigating how to affect the
