~

AAAI2021/tex/experiments.tex

@@ -109,11 +109,11 @@ Figure~\ref{fig:exp3} (a) shows that when $h_{size}$ equals to 1, the agent capa
 too low to handle languages. Figure~\ref{fig:exp3} (b) shows that when $h_{size}$
 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., $h_{size}$). Figure~\ref{fig:exp3} (c) to (h) show that the
+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
 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
+improvement for different languages is quite different. It is obvious that language with lower compositionality also requires higher agent
 capacity to train faster.
 
 

AAAI2021/tex/theory.tex

@@ -40,9 +40,7 @@ from $V$. The Listener $L$ receives $s$ and output predicted result $\hat{t}$,
 a single word (one-hot vector) selected from the Cartesian product of set two $V$s
 ($V\times V$), which representing all the meanings of two combined words from $V$.
 Please note that since $t$ and $\hat{t}$ have different length, we say
-$t=\hat{t}$ if $t$ expresses the same meaning as $\hat{t}$, e.g.,
-$t={[0,0,1],[0,1,0]}$ would be equal to $\hat{t}=[0,0,0,0,0,1]$ if they both mean ``red
-circle''. 
+$t=\hat{t}$ if $t$ expresses the same meaning as $\hat{t}$, e.g., ``red circle''. 
 
 
 \begin{figure*}[t]