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AAAI21_Emergent_language
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haoyifan
AAAI21_Emergent_language
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d843434e
Commit
d843434e
authored
Sep 10, 2020
by
YZhao
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AAAI2021/tex/experiments.tex
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d843434e
...
...
@@ -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 size
s
, 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 spoke
n
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) show
s
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.
...
...
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