% \begin{table}[]
% \centering
% \scriptsize
% \begin{tabular}{lrrrr}
% \toprule
% \textbf{Approch} & \multicolumn{1}{l}{\textbf{Simple}} & \multicolumn{1}{l}{\textbf{Jump}} & \multicolumn{1}{l}{\textbf{Right}} & \multicolumn{1}{l}{\textbf{Length}} \\
% \midrule
% \textbf{RNN \cite{lake2018generalization}}              & 99.7 & 1.7  & 2.5                   & 13.8 \\
% \textbf{CNN \cite{dessi2019cnns}}              & 100  & 69.2 & 56.7                  & 0    \\
% \textbf{Syntatic Attn \cite{russin2019compositional}}    & 100  & 91   & 28.9                  & 15.2 \\
% \textbf{CGPS \cite{li2019compositional}}             & 99.9 & 98.8 & 83.2                  & 20.3 \\
% \textbf{Equivar. Seq2Seq \cite{gordon2020permutation}} & 100  & 99.1 & 92                    & 15.9 \\
% \textbf{LANE \cite{liu2020compositional}}             & 100  & 100  & 100                   & 100  \\
% \textbf{Program Synth. \cite{nye2020learning}}   & 100  & 100  & 100                   & 100  \\
% \textbf{NeSS \cite{chen2020compositional}}             & 100  & 100  & 100                   & 100  \\
% \textbf{NQG-T5}           & 100  & 100  & \multicolumn{1}{l}{-} & 100  \\
% \textbf{T5-Base}          & 77.8 & 93.5 & 33.2                  & 13.6 \\
% \textbf{Neural QCFG}      & 96.1 & 94.3 & 96.85                 & 91.6 \\
% \bottomrule
% \end{tabular}
% \caption{Performance of NeuralQCFG on splits of SCAN. The baselines except for NQG-T5, T5-base are not reproduced by us and the values are cited from \cite{kim2021sequencetosequence}.}
% \label{tab:nqcfg-perf}
% \end{table}


\begin{table}
\scriptsize
\parbox{.43\linewidth}{
\begin{tabular}{lrrrr}
\toprule
\textbf{Approach} &
  \multicolumn{1}{l}{\textbf{Stan.}} &
  \multicolumn{1}{l}{\textbf{Temp.}} &
  \multicolumn{1}{l}{\textbf{Len.}} &
  \multicolumn{1}{l}{\textbf{TMCD}} \\
  \midrule
\textbf{Syn Attn \cite{russin2019compositional}} & 77.5 & 70.6 & 23.6 & 0.0    \\
\textbf{CGPS \cite{li2019compositional}}          & 62.1 & 32.8 & 9.3  & 32.3 \\
\textbf{T5-base}       & 92.9 & 91.1 & 48.0 & 53.9 \\
\textbf{NQG}           & 72.5 & 59.2 & 25.7 & 39.1 \\
\textbf{NQG-T5}        & 90.7 & 85.6 & 46.6 & 50.2 \\
\midrule
\end{tabular}
\caption{\footnotesize Performance of NQG-T5 and its baselines on the splits of GEOQUERY. Stan., Temp., and Len.~are abbreviations of standard, template, and length respectively.}
\label{tab:nqg-perf}
}
% \hfill
\hspace{1.5mm}
\parbox{.48\linewidth}{
\begin{tabular}{lrrrr}
\toprule
\textbf{Approch} & \multicolumn{1}{l}{\textbf{Simple}} & \multicolumn{1}{l}{\textbf{Jump}} & \multicolumn{1}{l}{\textbf{Right}} & \multicolumn{1}{l}{\textbf{Length}} \\
\midrule
\textbf{RNN \cite{lake2018generalization}}              & 99.7 & 1.7  & 2.5                   & 13.8 \\
\textbf{CNN \cite{dessi2019cnns}}              & 100.0  & 69.2 & 56.7                  & 0.0    \\
\textbf{Syn Attn \cite{russin2019compositional}}    & 100.0  & 91.0   & 28.9                  & 15.2 \\
\textbf{CGPS \cite{li2019compositional}}             & 99.9 & 98.8 & 83.2                  & 20.3 \\
\textbf{Eq. Seq2Seq \cite{gordon2020permutation}} & 100.0  & 99.1 & 92                    & 15.9 \\
\textbf{LANE \cite{liu2020compositional}}             & 100.0  & 100.0  & 100.0                   & 100.0  \\
\textbf{Program Syn. \cite{nye2020learning}}   & 100.0  & 100.0  & 100.0                   & 100.0  \\
\textbf{NeSS \cite{chen2020compositional}}             & 100.0  & 100.0  & 100.0                   & 100.0  \\
\textbf{NQG-T5}           & 100.0  & 100.0  & \multicolumn{1}{l}{-} & 100.0  \\
\textbf{T5-Base}          & 77.8 & 93.5 & 33.2                  & 13.6 \\
\textbf{Neural QCFG}      & 96.1 & 94.3 & 96.85                 & 91.6 \\
\bottomrule
\end{tabular}
\caption{\footnotesize Performance of NeuralQCFG on splits of SCAN. The baselines except for NQG-T5, T5-base are not reproduced by us and are cited from \cite{kim2021sequencetosequence}.}
\label{tab:nqcfg-perf}
}
\end{table}