## 7 Discussion
This strategy, however, leads to a mismatch between training and testing since, during testing, the model predicts the following word using the previously produced words from the model distribution. Since the model has never been exposed \begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Ref**} & \multicolumn{3}{c}{**Test Server**} & & & & & & \\ \cline{3-10}  & & **B-1** & **B-2** & **B-3** & **B-4** & **M** & **R** & **C** & **S** \\ \hline
2015 & Hard-Attention [3] & 70.5 & 52.8 & 38.3 & 27.7 & 24.1 & 51.6 & 86.5 & 17.2 \\  & NIC [4] & 71.3 & 54.2 & 40.7 & 30.9 & 25.4 & 53.0 & 94.3 & 18.2 \\  & Mind’s Eye [52] & - & - & - & 18.4 & 19.5 & - & 53.1 & - \\ \hline
2016 & ATT-FCN [47] & 73.1 & 56.5 & 42.4 & 31.6 & 25.0 & 53.5 & 94.3 & 18.2 \\ \hline
2017 & SCA-CNN [5] & 71.2 & 54.2 & 40.4 & 30.2 & 24.4 & 52.4 & 91.2 & - \\  & Adaptive Attention [76] & 74.8 & 58.4 & 44.4 & 33.6 & 26.4 & 55.0 & 104.2 & - \\ \hline
2018 & RFNet [49] & 80.4 & 64.9 & 50.1 & 38.0 & 28.2 & 58.2 & 122.9 & - \\  & Up-Down [56] & 80.2 & 64.1 & 49.1 & 36.9 & 27.6 & 57.1 & 117.9 & 21.5 \\ \hline
2019 & AoANet [79] & 81.0 & 65.8 & 51.4 & 39.4 & 29.1 & 58.9 & 126.9 & - \\  & Chen et al. [106] & 81.9 & 66.3 & 51.7 & 39.6 & 28.7 & 59.0 & 123.1 & - \\  & Obj-R+Rel-A [55] & 79.2 & 62.6 & 47.5 & 35.4 & 27.3 & 56.2 & 115.1 & - \\  & CAVP [7] & 80.1 & 64.7 & 50.0 & 37.9 & 28.1 & 28.1 & 121.6 & - \\  & MT [107] & 81.7 & 66.8 & 52.4 & 40.4 & 29.4 & 59.6 & 130.0 & - \\  & ETA [70] & 81.2 & 65.5 & 50.9 & 38.9 & 28.6 & 58.6 & 122.1 & - \\ \hline
2020 & M2 Transformer [10] & 81.6 & 66.4 & 51.8 & 39.7 & 29.4 & 59.2 & 129.3 & - \\  & MAD+SAP [6] & 80.5 & 65.1 & 50.4 & 38.4 & 28.6 & 58.7 & 125.1 & - \\  & X-LAN [60] & 81.4 & 66.5 & 52.0 & 40.0 & 29.7 & 59.5 & 130.2 & - \\  & X-Transformer [60] & 81.9 & 66.9 & 52.4 & 40.3 & 29.6 & 59.5 & 131.1 & - \\  & NG-SAN [58] & 80.8 & 65.4 & 50.8 & 38.8 & 29.0 & 58.7 & 126.3 & - \\  & He et al. [59] & 81.2 & - & - & 39.6 & 29.1 & 59.2 & 127.4 & - \\ \hline
2021 & (w/ MAC) [72] & 81.6 & 66.5 & 51.9 & 39.7 & 29.4 & 59.1 & 130.3 & - \\  & DLCT [73] & 82.4 & 67.4 & 52.8 & 40.6 & 29.8 & 59.8 & 133.3 & - \\  & DRT [71] & 82.7 & 67.7 & 53.1 & 40.9 & 29.6 & 59.8 & 132.2 & - \\ \hline
2022 & ReFormer [68] & 82.0 & - & - & 40.1 & 29.8 & 59.9 & 129.9 & - \\  & ArCo [62] & 83.4 & 68.8 & 54.3 & 42.0 & 30.6 & 60.8 & 138.5 & - \\ \hline
2023 & LATGeO [63] & 80.5 & 64.8 & 50.0 & 37.9 & 28.8 & 58.1 & 126.7 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The reported results from Microsoft COCO server. to its predictions, this _exposurebias_[109] causes error accumulation during generation at test time. During the fine-tuning phase, they use the REINFORCE method [110] to directly optimize the sequence level metrics and deal with the exposure bias issue. They particularly follow the self-critical technique [83]. Table 11 presents the results of studies that followed the two phases. **RQ6: What are the challenges in adopting the existing methods for image captioning in Arabic? ** It is possible to attain state-of-the-art outcomes by adapting English captioning models to Arabic utilizing public datasets [20]. The real challenge is using a high-quality, large, and varied database that contains rich information covering many aspects. Then, the appropriate preprocessing steps should be applied to reduce the complexity of the Arabic language, such as cleaning the text and applying normalization and tokenization. These seem like simple steps, but they significantly impact the model's performance and the generation of high-quality captions.