up

AAAI2021/tex/introduction.tex

@@ -43,7 +43,7 @@ More recently, measuring the compositionality \note{xxxxx}.}
 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{The basic intuition is that high compositional symbolic
+ease-of-teaching~\cite{}. \note{The possible intuition is that high compositional symbolic
 language cannot emerge without induction in existing multi-agent environment.}
 Figure~\ref{fig:induction} reports the compositionality when training two agents in the widely-used
 listener-speaker referential game, and it can be observed that \note{the compositionality
@@ -56,31 +56,13 @@ In other words, it is never clear whether \emph{natural}
 environment and agents are sufficient for achieving high compositionality. 
 
 In this paper, we are the first work to achieve high compositional
-symbolic language without any deliberately handcrafted induction. Concretely, 
-by thoroughly analyzing the compositionality of emerged symbolic
-language after removing the inductions in the most widely-used listener-speaker 
-referential game framework, we observe that the agent capacity 
-plays a key role in improving the compositionality, and thus we can achieve
- a high compositional symbolic language in such a natural environment. 
-
-%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.
-
-The quantitative relationship between the agent capacity and the compositionality 
-of symbolic language is revealed both theoretically and experimentally.
+symbolic 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,
+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 both theoretically and experimentally.
 %theoretically
 Regarding the theoretical analysis, we use the
 \note{Markov Series Channel (MSC)~\cite{} to model the language transmission process and a
@@ -90,10 +72,11 @@ Regarding the experimental validation, 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 theoretical analysis and
-  experimental results lead to a counter-intuitive conclusion that lower agent
-  capacity facilitates the emergence of symbolic language with higher
-  compositionality.
+Both the theoretical analysis and experimental results lead to a counter-intuitive 
+conclusion that \emph{lower agent capacity facilitates the emergence of symbolic language 
+with higher compositionality}. \note{Therefore, by only reducing the agent capacity, we 
+can generate a higher compositional symbolic language in such a natural environment 
+with a higher probability.}
   
 
 %Prior studies focus on investigating how to affect the