# Zero-Shot Relation Triple Extraction with Prompts for Low-Resource Languages
## 4 Experiments
### Main Experimental Results  
**Triplet Extraction:** Table 5 compares Relation Prompt-Uyghur to the baselines on Zero-Shot-Prompt-Relation Triplet Extraction (ZSP_RTE) for the UyRE-ZSP datasets. Regarding accuracy and F1 metrics, our method consistently outperforms the baseline methods in single-triplet and multi-triplet evaluations. Uyghur is one of the most morphologically complex and adhesive languages, in which the composition of words and their meanings rely on complex forms of affixation to represent them. The affixes not only change the root word's importance but also determine a word's role in a sentence. Therefore, the correct separation of stems and affixes is necessary to represent the word's true meaning as a whole correctly. Consequently, it requires extra processing of data; additional letters will be added or lost when the word root is extracted; this should be handled according to the Prompt to avoid the case of an extraction error.  
We have conducted comparative experiments between Zero-Shot-Prompt-RE and the baseline method in Table 5. Our method consistently outperformed the baseline method in terms of accuracy and F1 metrics in both single triad and multiple triplet results [10; 51]. We also note that Zero-Shot-Prompt-Relation Extraction (ZSP_RE) in Uyghur performs much better than a third-party Table Sequence [50; 51; 52], especially in multi-trigram extraction, suggesting that the Zero-Shot-Prompt-RE approach in Uyghur is practical. However, our proposed Uyghur entity and suffix processing, relation extractor, and decoder technique successfully resolve this difficulty by automatically listing and arranging many triples accurately at the time of inference, without the extracted entities carrying a suffix, as in previous work [39; 44].  
**Relation Extraction:** Zero-Shot-Prompt-RE for Uyghur includes information about entity pairs in the hints and supports zero-shot relation classification work without additional training. Table 6 shows that, compared to previous state-of-the-art ZS-BERT techniques [12],  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Method** & \multicolumn{2}{c}{**Single Triplet**} & \multicolumn{2}{c}{**Multi Triplet**} \\ \cline{3-6}  & **Acc.** & **Prec.** & **Rec.** & **F1** \\ \hline \multirow{5}{*}{m = 5} & Table Sequence [51] & 11.73 & 50.00 & 4.76 & 8.69 \\  & NoGen [10] & 8.32 & 83.33 & 6.17 & 11.49 \\  & Relation Prompt [6] & 13.65 & 71.25 & 26.51 & 38.64 \\  & Zero-Shot-RTE for Uyghur & 13.28 & 73.97 & 30.17 & 42.86 \\  & Zero-Shot-Prompt-RTE for Uyghur & 16.35 & 69.23 & 35.29 & 46.75 \\  & (Our suggested model) & & & & \\ \hline \multirow{5}{*}{m = 10} & Table Sequence [51] & 9.11 & 50.00 & 2.38 & 4.54 \\  & NoGen [10] & 6.58 & 75.00 & 5.83 & 0.11 \\  & Relation Prompt [6] & 14.56 & 71.43 & 9.26 & 16.39 \\  & Zero-Shot-RTE for Uyghur & 11.54 & 76.25 & 31.69 & 44.77 \\  & Zero-Shot-Prompt-RTE for Uyghur & 16.48 & 78.46 & 33.95 & 47.39 \\  & (Our suggested model) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results for our method in different relation types in Uyghur languages.  
our approach maintains relatively good F1 performance for our relational classification when the set of unseen relational labels increases. It may be possible to scale better to larger sets of unseen tags based on relational hints, which is very friendly for applications of relational triad extraction in open domains. The reason for the low recall is that when the samples are labeled, most positive samples are mistakenly labeled as negative samples. This makes it harder for the model to learn from positive examples, which lowers the accuracy of negative samples while positive samples are predicted as negative samples. To solve the problem, the next step is to fix the samples that were wrongly labeled and watch the results to see if anything has changed.