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

AAAI2021/ref.bib

@@ -13,6 +13,8 @@
   bibsource = {dblp computer science bibliography, https://dblp.org}
 }
 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Related Work%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
 @inproceedings{kottur-etal-2017-natural,
     title = "Natural Language Does Not Emerge {`}Naturally{'} in Multi-Agent Dialog",
     author = "Kottur, Satwik  and
@@ -93,3 +95,63 @@
   journal={arXiv preprint arXiv:2004.09124},
   year={2020}
 }
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+@article{DBLP:journals/corr/LazaridouPB16b,
+  author    = {Angeliki Lazaridou and
+               Alexander Peysakhovich and
+               Marco Baroni},
+  title     = {Multi-Agent Cooperation and the Emergence of (Natural) Language},
+  journal   = {CoRR},
+  volume    = {abs/1612.07182},
+  year      = {2016},
+  url       = {http://arxiv.org/abs/1612.07182},
+  archivePrefix = {arXiv},
+  eprint    = {1612.07182},
+  timestamp = {Mon, 13 Aug 2018 16:47:57 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/LazaridouPB16b.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+
+@article{bogin2018emergence,
+  title={Emergence of Communication in an Interactive World with Consistent Speakers},
+  author={Bogin, Ben and Geva, Mor and Berant, Jonathan},
+  journal={arXiv},
+  pages={arXiv--1809},
+  year={2018}
+}
+
+@inproceedings{jaques2019social,
+  title={Social influence as intrinsic motivation for multi-agent deep reinforcement learning},
+  author={Jaques, Natasha and Lazaridou, Angeliki and Hughes, Edward and Gulcehre, Caglar and Ortega, Pedro and Strouse, DJ and Leibo, Joel Z and De Freitas, Nando},
+  booktitle={International Conference on Machine Learning},
+  pages={3040--3049},
+  year={2019},
+  organization={PMLR}
+}
+
+@article{mul2019mastering,
+  title={Mastering emergent language: learning to guide in simulated navigation},
+  author={Mul, Mathijs and Bouchacourt, Diane and Bruni, Elia},
+  journal={arXiv preprint arXiv:1908.05135},
+  year={2019}
+}
+
+@inproceedings{kharitonov2019egg,
+  title={EGG: a toolkit for research on Emergence of lanGuage in Games},
+  author={Kharitonov, Eugene and Chaabouni, Rahma and Bouchacourt, Diane and Baroni, Marco},
+  booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations},
+  pages={55--60},
+  year={2019}
+}
+
+@article{labash2020perspective,
+  title={Perspective taking in deep reinforcement learning agents},
+  author={Labash, Aqeel and Aru, Jaan and Matiisen, Tambet and Tampuu, Ardi and Vicente, Raul},
+  journal={Frontiers in Computational Neuroscience},
+  volume={14},
+  year={2020},
+  publisher={Frontiers Media SA}
+}
+

AAAI2021/tex/experiments.tex

@@ -82,7 +82,7 @@ In summary, lower agent capacity improves the possibility of
 emerging high compositional symbolic language.
 
 
-
+\subsection{Ratio of high compositional language.}
 We further breakdown our results to investigate the importance of agent capacity
 to the compositionality of symbolic language. Figure~\ref{fig:exp2} reports the
 ratio of high compositional symbolic language in all emerged languages,
@@ -137,7 +137,7 @@ capacity.
 %\end{figure}
 
 
-\textbf{Breakdown into language teaching.}
+\subsection{Breakdown into language teaching.}
 We further breakdown the learning process to investigate the language teaching
 scenario, where the Speaker teaches the Listener its fixed symbolic language.
 We define three symbolic languages in different compositionality for Speaker to

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
@@ -40,9 +40,9 @@ vocabulary can express almost infinite concepts.}
 \begin{figure}[t]
   \centering
   \includegraphics[width=\columnwidth]{fig/Figure1_motivation.pdf}
-  \caption{The distribution of compositionality for 100 emerged symbolic
+  \caption{The distribution of compositionality for 100 emergent
     languages without
-    any induction. It can be observed that high compositional symbolic language
+    any induction. It can be observed that high compositional language
     seldom emerged (e.g., $<5\%$ for compositionality $>0.99$). Moreover, varying
     the vocabulary size does not affect the compositionality notably.}
   \label{fig:induction}
@@ -71,32 +71,34 @@ vocabulary can express almost infinite concepts.}
     \end{tabular}
   \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.}
+Prior studies focus on achieving high compositional language
+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 language 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.
-Yet, few works investigate the emergence of high compositional symbolic language
+Yet, few works investigate the emergence of high compositional language
 \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.
 
 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
@@ -106,16 +108,16 @@ Regarding the theoretical analysis, we propose
 a novel mutual information-based metric to measure the compositionality quantitatively.
 %experimentally
 Regarding the experimental validation, we exploit the relationship between agent
-capacity and the compositionality of symbolic language that emerged
-\emph{naturally} in our experiments.
+capacity and the compositionality of \emph{naturally} emergent language
+in our experiments.
 %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 
+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
@@ -208,16 +210,16 @@ can generate a higher compositional symbolic language with a higher probability.
 In this paper, we made the following contributions:
 \begin{itemize}[topsep=0pt,itemsep=0cm]
 \item To our best knowledge, we are the first work to successfully achieve
-high compositional symbolic
+high compositional emergent
 language naturally, without any deliberately handcrafted induction.
-\item We analyze the compositionality of emerged symbolic language
+\item We analyze the compositionality of emergent language
 after removing deliberately handcrafted inductions.
 \item We propose a novel mutual information-based metric to measure the
 compositionality quantitatively, which is more reasonable.
 \item We experimentally exploited the relationship between agent
 capacity. Both theoretical analysis and
 experimental results lead to a counter-intuitive conclusion that lower agent
-capacity facilitates the emergence of symbolic language with higher
+capacity facilitates the emergence of language with higher
 compositionality.
 \end{itemize}
 
@@ -227,6 +229,6 @@ Section~\ref{sec:relatedwork} summarizes the related works.
 Section~\ref{sec:thory}
 introduces the experimental setup used in this study. Section~\ref{sec:mis}
 describes our proposed novel mutual-information-based metric for measuring
-the compositionality of symbolic language. Section~\ref{sec:exp} gives the
+the compositionality of emergent language. Section~\ref{sec:exp} gives the
 experimental results of the exploration for the relationship between agent
 capacity and compositionality. Section~\ref{sec:con} concludes this paper.