up

AAAI2021/paper.tex

@@ -95,7 +95,7 @@
 % articles, conjunctions, and prepositions are lower case unless they
 % directly follow a colon or long dash
 
-\title{Revisiting the Natural Emergence of Symbolic Language with Agent Capacity}
+\title{Enabling the Emergence of Symbolic Language without Handcrafted Inductions}
 \author{
     %Authors
     % All authors must be in the same font size and format.
@@ -177,7 +177,7 @@
   inductions. 
   
   In this paper, we are the first to successfully achieve high compositional symbolic
-  language in a purely \emph{natural} manner.
+  language in a \emph{natural} manner without handcrafted inductions.
   Initially, by thoroughly investigating the compositionality of emerged symbolic
   language after removing the \emph{deliberately handcrafted}
   inductions, we observe that the agent capacity plays a key role in
@@ -194,7 +194,7 @@
   experimental results lead to a counter-intuitive conclusion that lower agent
   capacity facilitates the emergence of symbolic language with higher
   compositionality. \note{Based on our conclusion, we can generate higher
-  compositional symbolic language with a high probability.}
+  compositional symbolic language with a higher probability.}
 
 
 %  The natural emergence of symbolic languages with high compositionality has

AAAI2021/tex/introduction.tex

 \section{Introduction}
 \label{sec:introduction}
 
-The emergence of symbolic language has always been an important and controversial
-issue. This problem attracts attentions from a broad range of communities,
+The emergence of symbolic language has always been an important issue, 
+which attracts attentions from a broad range of communities,
 including philology~\cite{}, biology~\cite{}, and computer
 science~\cite{}. Especially in computer science, efforts in recent years try to explore
-the emergence of symbolic language in virtual, multi-agent environments, where
-agents are trained to communicate with neural network based method, i.e., deep
-reinforcement learning~\cite{}. For example, \note{XXXX}
+the emergence of symbolic 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,
 %referential game~\cite{} and multi-agent reinforcement learning (MARL)~\cite{}, based on
 %the environment setting.  
 
 
-Compositionality is widely used and
-taken as an important metric to evaluate the emerged symbolic language.
-Originally, compositionality is a principle that
-whether the meaning of a complex expression (e.g, phase), which is assembled out of the
+The quality of emergent symbolic 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
 given set of simple components (e.g., symbols), can be determined by its
 constituent components and the rules that combines them~\cite{}.
 \note{For example, the expression "AAAI is a conference'' consists of two
-meaningful words ``AAAI'' and ``conference'', and a rule for definition (``is'').}
-More recently, measuring the compositionality \note{xxxxx}.
+meaningful words ``AAAI'' and ``conference'', and a rule for definition (``is'').
+More recently, measuring the compositionality \note{xxxxx}.}
 
 
 %It
@@ -41,28 +40,41 @@ More recently, measuring the compositionality \note{xxxxx}.
   \label{fig:symbols}
   \end{figure}
 
-Prior studies focus on achieving high compositionality
+Prior studies focus on achieving high compositional symbolic language 
 through \emph{deliberately handcrafted} inductions, e.g., small vocabulary
 sizes~\cite{}, memoryless~\cite{}, carefully constructed rewards~\cite{}, and
-ease-of-teaching~\cite{}. \note{xxxxxxx}
-However, these unnatural inductions prevent us from better understanding the mystery of
+ease-of-teaching~\cite{}. \note{The basic intuition is that high compositional symbolic
+language cannot emerge without induction in existing multi-agent environment.}
+Figure~\ref{fig:xx} reports the compositionality when training two agents in the widely-used
+listener-speaker referential game, and it can be observed that \note{the compositionality
+of emerged symbolic language is extremely low}.
+Though such unnatural inductions are crucial, they prevent us from better understanding the mystery of
 the emergence of language and even intelligence among our pre-human ancestors.
 Yet, few works investigate the emergence of high compositional symbolic language
-\emph{naturally}, i.e., without \emph{deliberately
-  handcrafted} inductions.
-As a results, it is never clear whether \emph{natural}
-environment and agent are sufficient for compositionality. 
+\emph{naturally}, i.e., without handcrafted inductions.
+In other words, it is never clear whether \emph{natural}
+environment and agents are sufficient for achieving high compositionality. 
 
-In this work, we focus on the natural emergence of high compositional symbolic language
-naturally without any handcrafted induction.
-Initially, we thoroughly analyze the compositionality of emerged symbolic
-language after removing the \emph{deliberately handcrafted}
+In this work, we are the first to achieve high compositional
+symbolic language without any handcrafted induction. Concretely, we first
+thoroughly analyze the compositionality of emerged symbolic
+language by removing the \emph{deliberately handcrafted}
 inductions. Figure~\ref{fig:comp} reports the compositionality when train two
 agents in a listener-speaker referential game. It can be observed that \note{it
   is challenging to achieve high compositionality without induction as
   xxxxxx}. Moreover, we observe that the agent capacity plays a key role in
 compositionality, see Figure xxx.
 
+%focus on the natural emergence of high compositional symbolic language
+%naturally without any handcrafted induction.
+%Initially, we thoroughly analyze the compositionality of emerged symbolic
+%language after removing the \emph{deliberately handcrafted}
+%inductions. Figure~\ref{fig:comp} reports the compositionality when train two
+%agents in a listener-speaker referential game. It can be observed that \note{it
+%  is challenging to achieve high compositionality without induction as
+%  xxxxxx}. Moreover, we observe that the agent capacity plays a key role in
+%compositionality, see Figure xxx.
+
 We reveal and characterize the quantitative relationship
 between the agent capacity and the compositionality of symbolic language both
 theoretically and experimentally.