Merge branch 'master' of http://62.234.201.16/hao/AAAI21_Emergent_language

AAAI2021/paper.tex

@@ -178,18 +178,18 @@
  The emergence of symbolic languages with high compositionality has
   attracted extensive attention from a broad range of communities. Existing
   studies achieve high compositionality through \emph{deliberately handcrafted}
-  inductions (e.g., small vocabulary sizes, carefully constructed distractors, 
-  and ease-of-teaching) in multi-agent learning, which are unnatural. 
+  inductions (e.g., small vocabulary sizes, addtional rewards, constructed 
+  loss functions and ease-of-teaching) in multi-agent learning, which are unnatural. 
   Yet, few studies investigate the emergence of symbolic language with high
   compositionality \emph{naturally}, i.e., without deliberately handcrafted
   inductions. 
   
-  In this paper, we are the first to successfully achieve high compositional symbolic
-  language in a \emph{natural} manner without handcrafted inductions.
+  In this paper, \note{we are the first to successfully achieve high compositional
+  symbolic language} in a \emph{natural} manner without handcrafted inductions.
   Initially, by 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
-  compositionality. We further reveal and characterize the quantitative relationship
+  compositionality. We further reveal and characterize the \note{quantitative relationship}
   between the agent capacity and the compositionality of symbolic language, with
   a novel mutual information-based metric for the compositionality. 
   % both theoretically and experimentally.

AAAI2021/tex/relatedwork.tex

@@ -33,19 +33,26 @@ agent itself. \rmk{this should be largely emphasized.}
 %measure
 To measure the compositionality of emerged symbolic language, many metrics are
 proposed~\cite{}. 
-Widely accepted metrics can be classified into two categories, measuring
-positive signaling~\cite{} and measuring positive listening~\cite{}. The former
-metrics measure the relationship between spoken symbols and received concepts
-\rmk{not clear}, from the perspective of \emph{speakers}. 
-For example,.
-The latter metrics measure the relationship between received symbols and
-predicted concepts \rmk{not clear}, from the perspective of \emph{listeners}.
-For example,. 
-However, these metrics are not appropriate, for they only measure
-compositionality of symbolic language in \emph{unilateral} role\rmk{not sure},
-either speakers or listeners. They can not measure the degree of \emph{bilateral}
-understanding between speakers and listeners, i.e., the concept-symbol mapping
-consistency between speakers and listeners.
+%Widely accepted metrics can be classified into two categories, measuring
+%positive signaling~\cite{} and measuring positive listening~\cite{}. The former
+%metrics measure the relationship between spoken symbols and received concepts
+%\rmk{not clear}, from the perspective of \emph{speakers}. 
+%For example,.
+%The latter metrics measure the relationship between received symbols and
+%predicted concepts \rmk{not clear}, from the perspective of \emph{listeners}.
+%For example,. 
+%However, these metrics are not appropriate, for they only measure
+%compositionality of symbolic language in \emph{unilateral} role\rmk{not sure},
+%either speakers or listeners. They can not measure the degree of \emph{bilateral}
+%understanding between speakers and listeners, i.e., the concept-symbol mapping
+%consistency between speakers and listeners.
+At the initial stage, many researches only analyzed the language compositionality qualitatively.
+For example, ~\citet{choi2018compositional} printed the agent messages with the letter `abcd' at some training round, and directly analyzed the compositionality on these messages.
+~\citet{kottur-etal-2017-natural} introduced the dialog tree to show the evolution of language compositionality during the trianing process.
+Latter, some quantitative metrics are explored.
+The topographic similarity\cite{lazaridou2018emergence} is introduced to measure the distances between all the possible pairs of meanings and the corresponding pairs of signals.
+\citet{chaabouni2020compositionality} proposed the positional disentanglement and the bag-of-symbols disentanglement. The positional disentanglement measures whether symbols in specific postion clearly relate to the specific attribute of the input object. The bag-of-symbols measure the permutation-invariant characteristic of a language.
+