## 6 Results and Discussion
### Use Case 1: Only Monolingual (Urdu) Data Available  
Table 3 shows the performance of our students models. Note that the same hyperparameter configuration was chosen for all our student models and the baseline (see the last row of Table 2), ensuring that their performance results are directly comparable. As mentioned above, all student models, including the baseline (shown in the last row of Table 2), were trained for a maximum of two epochs. The CO\({}_{2}\) emissions generated during training of the student models were recorded. The last column of Table 3 shows the CO\({}_{2}\) emissions generated during the training of our models. The student models in Table 3 were built using distilled data without any bilingual parallel sentences. As mentioned in Section 5.2, we considered different threshold values for the estimated confidence scores produced by the teacher model in order to filter out noisy instances from the distilled data. Specifically, we used a grid of threshold values corresponding to the first quartile (0.1295), second quartile (0.2175), and third quartile (0.3802). Our aim was to examine the relationship between the teacher's confidence and the quality of the resulting student models. This also helped us to identify suitable threshold values for confidence-based filtering in low-resource KD scenarios. From now on, we refer to the student models built using the standard and confidence estimation-based KD setups as SKD and CEKD, respectively. The SKD model serves as the KD baseline model in our experimental setup. As can be seen from Table 3, the student models outperform the vanilla baseline across all metrics on IN22-Gen (the baseline model (Baseline; cf. Table 3) trained on the original parallel corpus is not directly comparable to the student models reported in this section, as this use case does not include access to the parallel corpus used for training in that case). The students models outperform the vanilla baseline across chrF and COMET on the FLORES-22 test data. SKD achieved a 0.22 BLEU point (corresponding to 0.59% relative) improvement on IN22-Gen compared to the vanilla baseline. The best-performing CEKD model was the one trained with the threshold set to the third quartile, which achieved a 0.69 BLEU point improvement (corresponding to a 1.84% relative gain) on IN22-Gen compared to the vanilla baseline. This CEKD model outperformed SKD with reasonable margins in BLEU, chrF, and COMET scores. Similar improvements were generally observed on the FLORES-200 test set as well. Figure 6 presents six plots showing how the student models performed at different confidence thresholds on both test sets in terms of BLEU, chrF, and COMET. The student model corresponding to a confidence threshold of 0.0 represents SKD, \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & & **SacreBLEU** & **chrF** & **COMET** & **Train** & **CO\({}_{2}\)** & **TT** \\ \hline Vanilla Baseline & IN22-Gen & 37.48 & 63.31 & 83.73 & - & - & - \\  & FLORES-200 & 31.87 & 58.80 & 82.63 & & & \\ \hline SDD & IN22-Gen & 37.70 & 65.46 & 84.65 & 188,130 & 0.69 & 4.0 \\  & FLORES-200 & 31.19 & 60.87 & 83.72 & & & \\ \hline FDD & IN22-Gen & 37.85 & 65.42 & 84.62 & 153,754 & 0.47 & 2.7 \\ (1 st Quartile) & FLORES-200 & 30.89 & 60.65 & 83.66 & & & \\ \hline FDD & IN22-Gen & 37.74 & 65.51 & 84.74 & 85,298 & 0.25 & 1.4 \\ (2 nd Quartile) & FLORES-200 & 31.02 & 60.81 & 83.68 & & & \\ \hline FDD & IN22-Gen & 38.17 & 65.94 & 85.10 & 49,517 & 0.14 & 0.8 \\ (3 rd Quartile) & FLORES-200 & 31.38 & 61.15 & 84.01 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of the student models. SDD: Standard Distilled Data, FDD: Filtered Distilled Data. CO\({}_{2}\) is calculated in KGs. TT is the training time calculated in hours. All student models used the same architecture and had identical size and memory usage. while the student models corresponding to the confidence thresholds of the first (0.1295), second (0.2175), and third (0.3802) quartiles represent CEKD.