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AAAI2021/tex/experiments.tex

@@ -98,7 +98,7 @@ Notably, when $h_{size}$ is large enough (e.g., $>40$), high compositional
 symbolic language is hard to emerge in a natural referential game, for
 easy-to-emerge low compositional symbolic language is sufficient in scenarios of
 referential game.
-On other side, agents are enforced to use compositionality to express
+On the other side, agents are enforced to use compositionality to express
 more meanings, for the constraint from low capacity.
 
 
@@ -107,8 +107,8 @@ Additionally, we also perform $\chi^2$ test to check the statistical
 significance between the high compositionality and agent
 capacity. Table~\ref{tab:exp10} reports the $\chi^2$ test results for
 $\mathit{MIS}>0.99$ and $\mathit{MIS}>0.9$, respectively. It can be observed that
-for different vocabulary size, the p-value is always less than 0.05, which means
-the high compositionality has statistical significance related to agent
+for different vocabulary sizes, the p-value is always less than 0.05, which means
+the high compositionality has a statistical significance related to agent
 capacity.
 
 
@@ -142,10 +142,10 @@ 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
 teach, i.e., high (LA, $\mathit{MIS}=1$), mediate (LB, $\mathit{MIS}=0.83$), low (LC, $\mathit{MIS}=0.41$), see
-Figure~\ref{fig:bench}.  
+Figure~\ref{fig:bench}.
 
-Figure~\ref{fig:exp3} reports the accuracy of Listener, i.e., ratio of the correctly
-predicted symbols spoken by Speaker ($t=\hat(t)$), which varies with the
+Figure~\ref{fig:exp3} reports the accuracy of Listener, i.e., the ratio of the correctly
+predicted symbols spoke by Speaker ($t=\hat(t)$), which varies with the
 training iterations under different agent capacities.
 Figure~\ref{fig:exp3} (a) shows that when $h_{size}$ equals to 1, the agent capacity is
 too low to handle languages. Figure~\ref{fig:exp3} (b) shows that when $h_{size}$
@@ -153,7 +153,7 @@ equals to 2, agent can only learn $LA$ whose compositionality (i.e. \emph{MIS})
 is highest in all three languages. Combing these two observations, we can infer that
 language with lower compositionality requires higher agent capacity to ensure
 communicating successfully (i.e., $h_{size}$).
-Additionally, Figure~\ref{fig:exp3} (c)$\sim$(h) show that the
+Additionally, Figure~\ref{fig:exp3} (c)$\sim$(h) shows that the
 higher agent capacity causes a faster training process for all three languages, but the
 improvement for different languages is quite different. It is obvious that language with lower compositionality also requires higher agent
 capacity to train faster.