Previous works focus on the external environmental factors that impact the
compositionality of emerged symbolic language.
Some significant works on studying the external environmental factor on the compositionality of emergent language are summarized on Table~\ref{tab:rel}.
For example, ~\citet{kirby2015compression} explored how the pressures for expressivity and compressibility lead the structured language.
~\citet{kottur-etal-2017-natural} constrained the vocabulary size and whether the listener has memory to coax the compositionality of the emergent language.
~\citet{lazaridou2018emergence} showed that the degree of structure found in the input data affects the emergence of the symbolic language.
~\citet{li2019ease} studied how the pressure, ease of teaching, impact on the iterative language of the population regime.
~\citet{evtimova2018emergent} designed a novel multi-modal scenarios, which the speaker and the listener should access to different modalities of the input object, to explore the language emergence.
Such factors are deliberately designed, which are too ideal to be true in
the real world. None of these works realizes the importance of model capacity of
agent itself. \rmk{this should be largely emphasized.}
Such factors are deliberately designed, which are too ideal to be true in the real world.
In this paper, these handcrafted inductions above are all removed, and the high compostional language is leaded only by the agent capacity. \rmk{this should be largely emphasized.}
\begin{table*}[htbp]
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@@ -24,11 +24,12 @@ agent itself. \rmk{this should be largely emphasized.}
Works & Handcrafted induction & Compositionality\\
\midrule
\cite{kirby2015compression}&Expressivity and compressibility&Qualitative, Speaker\\
%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
...
...
@@ -56,7 +57,8 @@ For example, ~\citet{choi2018compositional} printed the agent messages with the
~\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.
\citet{chaabouni2020compositionality} proposed the positional disentanglement, which measures whether symbols in specific postion clearly relate to the specific attribute of the input object.
From Table~\ref{tab:rel}, most metrics are proposed on the sight of the speaker. In our view, human begings developed the language based on both the speakers and the listener. Only one research of \cite{choi2018compositional} in Table~\ref{tab:rel} qualitatively considered from the sight of the speaker and the listener. In this paper, we propose a novel quatitative metric from both the speaker's sight and the listener's sight.
