# Recognizing Textual Inference in Mongolian Bar Exam Questions
## 3 NLI of Mongolian Bar Exam Questions
### Language Modeling for Predicting NLI Labels in Mongolian Bar Exam Questions
\begin{table}
\begin{tabular}{c c c c|c c c c} \hline \hline
**Label** & **Precision** & **Recall** & **F1 score** & **Label** & **Precision** & **Recall** & **F1 score** \\ \hline Contradiction & 0.5738 & **0.8333** & **0.6796** & Contradiction & **0.5385** & 0.5000 & 0.5185 \\ Entailment & **0.6957** & 0.3810 & 0.4923 & Entailment & 0.5333 & **0.5714** & **0.5517** \\ Accuracy & & & 0.6071 & Accuracy & & & 0.5357 \\ Macro average & 0.6347 & 0.6071 & 0.5860 & Macro average & 0.5359 & 0.5357 & 0.5351 \\ Weighted average & 0.6347 & 0.6071 & 0.5860 & Weighted average & 0.5359 & 0.5357 & 0.5351 \\ \hline \multicolumn{6}{c|}{**a** mongolian-roberta-base [50]} & \multicolumn{6}{c}{**b** mongolian-roberta-large [51]} \\ \hline
**Label** & **Precision** & **Recall** & **F1 score** & **Label** & **Precision** & **Recall** & **F1 score** \\ \hline Contradiction & 0.5882 & **0.7143** & **0.6452** & Contradiction & 0.6600 & **0.7857** & **0.7174** \\ Entailment & **0.6364** & 0.5000 & 0.5600 & Entailment & **0.7353** & 0.5952 & 0.6579 \\ Accuracy & & & 0.6071 & Accuracy & & & 0.6905 \\ Macro average & 0.6123 & 0.6071 & 0.6026 & Macro average & 0.6976 & 0.6905 & 0.6876 \\ Weighted average & 0.6123 & 0.6071 & 0.6026 & Weighted average & 0.6976 & 0.6905 & 0.6876 \\ \hline \multicolumn{6}{c|}{**c** alert-mongolian [52]} & \multicolumn{6}{c}{**d** bert-base-mongolian-cased [55]} \\ \hline
**Label** & **Precision** & **Recall** & **F1 score** & **Label** & **Precision** & **Recall** & **F1 score** \\ \hline Contradiction & 0.7174 & **0.7857** & **0.7500** & Contradiction & **0.7500** & 0.5714 & 0.6486 \\ Entailment & **0.7632** & 0.6905 & 0.7250 & Entailment & 0.6538 & **0.8095** & **0.7234** \\ Accuracy & & & 0.7381 & Accuracy & & & 0.6905 \\ Macro average & 0.7403 & 0.7381 & 0.7375 & Macro average & 0.7019 & 0.6905 & 0.6860 \\ Weighted average & 0.7403 & 0.7381 & 0.7375 & Weighted average & 0.7019 & 0.6905 & 0.6860 \\ \hline \multicolumn{6}{c|}{**e** bert-large-mongolian-uncased [56]} & \multicolumn{6}{c}{**f** bert-base-mullilingual-cased [59]} \\ \hline
**Label** & **Precision** & **Recall** & **F1 score** & **Label** & **Precision** & **Recall** & **F1 score** \\ \hline Contradiction & **0.7273** & 0.5714 & 0.6400 & Contradiction & **0.7317** & 0.7143 & 0.7229 \\ Entailment & 0.6471 & **0.7857** & **0.7097** & Entailment & 0.7209 & **0.7381** & **0.7294** \\ Accuracy & & & 0.6786 & Accuracy & & & 0.7262 \\ Macro average & 0.6872 & 0.6786 & 0.6748 & Macro average & 0.7263 & 0.7262 & 0.7262 \\ Weighted average & 0.6872 & 0.6786 & 0.6748 & Weighted average & 0.7263 & 0.7262 & 0.7262 \\ \hline \multicolumn{6}{c|}{**g** bert-large-mongolian-cased [77]} & \multicolumn{6}{c}{**h** bert-base-mongolian-uncased [78]} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The performance outcomes of the existing models in recognizing textual inference in Mongolian bar exam questions. Figure 1: _Cont._Fine-Tuning Pretrained Transformer Models in Recognizing Textual Inference in Mongolian Bar Exam Questions  
The existing pretrained models introduced in Section 3.3 were fine-tuned for recognizing textual inference in Mongolian bar exam questions by unfreezing and retraining. The legal domain has specific vocabulary and characteristics in legal texts. Thus, as discussed in Section 3.3, the performance outcomes of the existing pretrained models in Mongolian legal texts were not decent. Fine-tuning allows us to adapt the feature representations in the existing pretrained models to the new samples, i.e., the NLI dataset of Mongolian bar exam questions for making the existing pretrained models more applicable to the Mongolian legal NLI task. The setup and dataset are described below.