haoyifan update introduction

AAAI2021/tex/introduction.tex

 \section{Introduction}
 \label{sec:introduction}
 
-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,
@@ -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
@@ -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.
@@ -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
+of emergent language is characterized, with a novel mutual information-based
 metric for the compositionality.
 %both theoretically and experimentally.
 %theoretically
@@ -112,10 +114,10 @@ capacity and the compositionality of symbolic language that emerged
 %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 
+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.}
+can generate a more compositional language with a higher probability.}
   
 
 %Prior studies focus on investigating how to affect the