alabnii/sciclaimeval-shared-task huggingface dataset:
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\multirow{2}{*}{\textbf{Model}} & \multicolumn{5}{c|}{\textbf{Dev Set}} & \multicolumn{5}{c}{\textbf{Test Set}} \\
& \multicolumn{1}{c}{Bleu$_1$} & \multicolumn{1}{c}{Bleu$_2$} & \multicolumn{1}{c}{Bleu$_3$} & \multicolumn{1}{c}{Bleu$_4$} & \multicolumn{1}{c|}{ROUGE} & \multicolumn{1}{c}{Bleu$_1$} & \multicolumn{1}{c}{Bleu$_2$} & \multicolumn{1}{c}{Bleu$_3$} & \multicolumn{1}{c}{Bleu$_4$} & \multicolumn{1}{c}{ROUGE} \\
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G2S & 16.11 & 8.77 & 5.97 & 4.49 & 16.19 & 16.29 & 9.20 & 6.37 & 4.93 & 16.73\\
T2S & 15.65 & 8.35 & 5.76 & 4.44 & 15.65 & 14.12 & 7.76 & 5.53 & 4.39 & 14.82\\
TG2S & \textbf{17.25} & \textbf{10.17} & \textbf{7.04} & \textbf{5.32} & \textbf{17.85} & \textbf{17.18} & \textbf{10.39} & \textbf{7.39} & \textbf{5.76} & \textbf{17.64}\\
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\caption{Table 4: Back translation results obtained from the generative models when using manual features and facial landmarks and AUs. Our proposed model has the highest scores in all metrics compared to the models using only gloss or text.}
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