# Zero-Shot Relation Triple Extraction with Prompts for Low-Resource Languages
## 5 Analysis  
### Ablation Study  
The generated data's accuracy is crucial to our Zero-Shot-Prompt-Relation Triplet Extraction (ZSP_RTE) technique's success. Therefore, we contrast a variety of genuine and artificial data sets. We count the number of distinct words and other items in the texts. The accuracy of the data generated is critical to the success of our Zero-Shot Prompt-Relation Triplet extraction technique. Therefore, we compared the kinds of natural and artificial data samples and performed comparative experiments. Table 7 shows that the synthetic data sentences have more entities with a variety of unique components. However, the generated sentences have fewer individual words and less lexical diversity.  
However, the generated sentences have a lesser vocabulary diversity. On the other hand, non-entity words account for the majority of the total uncommon words.  
Our suggested Zero-Shot-Prompt-Relation Triplet Extraction approach relies significantly on the quality of the produced data. Thus, we examine a variety of samples of actual and fake data. Specifically, we count the unique words and items in the texts. We used the UyRE-ZSP validation set to produce five-label sentences and an equivalent number of synthetic phrases for comparison. Table 7 demonstrates that the created sentences include various unique items.  
Nevertheless, the resulting phrases include fewer unique words overall, which may be explained by the fact that entity names are often unique. The generator language model has been exposed to many unique entity names during extensive pre-training. On the other hand, non-entity words account for the majority of the overall number of unique words. By employing prompts to condition the production of sentences explicitly for unknown connection labels, the output sentences may include less context-specific information.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Method** & **Prec.** & **Rec.** & **F1** \\ \hline \multirow{4}{*}{m = 5} & R-BERT [51] & 88.40 & 13.40 & 23.28 \\  & ZS-BERT [3] & 73.81 & 32.63 & 45.25 \\  & Relation Prompt [6] & 66.67 & 53.84 & 59.57 \\  & Zero-Shot-Prompt-RE for Uyghur & 70.89 & 57.55 & 63.35 \\  & (Our suggested model) & & & \\ \hline \multirow{4}{*}{m = 10} & R-BERT [51] & 88.73 & 12.67 & 22.18 \\  & ZS-BERT [3] & 74.32 & 30.55 & 43.30 \\ \cline{1-1}  & Relation Prompt [6] & 82.25 & 38.75 & 52.68 \\ \cline{1-1}  & Zero-Shot-Prompt-RE for Uyghur & 82.02 & 48.39 & 60.87 \\ \cline{1-1}  & (Our suggested model) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results for our suggested method in Uyghur Zero-Shot-Prompt-Relation Extraction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Sentences** & **Entities** & **Words** \\ \hline Our Data & 2451 & 2859 & 10,356 \\ Generated Data & 2451 & 3122 & 7452 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Data diversity comparison.  
Table 8 shows the results of an ablation study that was performed to test how well our decoding method and task-specific fine-tuning worked for the Uyghur visible relation set on the multi-triple recombination Zero-Shot-Prompt-Relation Extraction. The comparison was conducted on the UygRE-ZSP validation set, which contains ten invisible identifiers. This large performance disparity indicates that triplet search decoding is essential for Zero-Shot-Prompt-Relation Triplet Extraction for Uyghur with multiple triplets, implying that the enumeration and classification of associative triplet candidates are of adequate quality. Second, we observe that the relation extractor's performance degrades significantly when the visible relation samples from the column set are not fine-tuned prior to the final fine-tuning of the synthetic samples generated by the invisible labels.  
When the size of the unseen label set, m, increases, our technique can retain a relatively good classification F1 performance, while Zero-Shot-Prompt-RE exhibits a more pronounced decline in performance.