# Can We Survive without Labelled Data in NLP? Transfer Learning for Open Information Extraction
## 6 Results and Evaluation
### Results of Transferring to OIE: Bio-Medical Domain  
In order to properly evaluate transfer learning results, we compared it with training and testing on the target task. Detailed results of the experiments can be found in Table 7, indicating the source task (training set) and the target task (testing set). The hyperparameters that achieved the highest scores are the ones reported in Table 7.  
OIE: Bio-Medical Domain Results Discussion  
Our system achieved the highest results using XLM-RoBERTa in all three experiments: transductive transfer learning, inductive transfer learning, and traditional learning, outperforming all other word embeddings.  
When our training set was composed entirely of news data, XLM-RoBERTa scored the highest F-measure of 64.4%, with 100 batches and 5 epochs. XLNet and GloVe achieved the same F-measure of 62.9% using the same number of batches and epochs, 100 and 5, respectively. Nevertheless, BERT achieved the lowest F-measure of 60%.  
In inductive transfer learning, a small amount of bio-medical data were inputted to the neural framework by sampling a random batch from the DDIExtraction 2013 training data using a 4:1 ratio, with bio-medical data having the lower ratio. A significant increase in the F-measure of 13.6% was attained in inductive transfer learning when comparing to transductive transfer learning. Using both XLM-RoBERTa and XLNet, our inductive transfer approach realized an F-measure of approximately 78%, with XLM-RoBERTa's precision surpassing XLNet's by 0.9%. BERT came in third and achieved 75.2%, while GloVe scored an F-measure of 73.7%.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Relations.**} & \multicolumn{2}{c}{**Number of Instances**} \\  & **Train Set** & **Test Set** \\ \hline
1. Cause-Effect & 485 & 228 \\
2. Instrument–Agency & 245 & 156 \\
3. Product–Producer & 320 & 231 \\
4. Entity–Origin & 398 & 258 \\
5. Entity–Destination & 392 & 252 \\
6. Component–Whole & 209 & 110 \\
7. Content–Container & 118 & 102 \\
8. Member–Collection & 345 & 233 \\
9. Message–Topic & 394 & 261 \\
**Total** & **2906** & **1831** \\ \hline \hline \end{tabular}
\end{table}
Table 6: List of predefined relations in the Semeval-2010 corpus and their number of occurrences.  
The results scored using traditional learning by training entirely on bio-medical data were only 1% higher than the results achieved using the inductive transfer learning technique. Once again, XLM-RoBERTa outperformed the other embeddings by scoring an F-measure of 79% using 100 batches and 15 epochs. Additionally, BERT achieved roughly the same F-measure as XLM-RoBERTa of 78.9%, using the same number of epochs and batches; however, it achieved a lower precision of 85.9%. It is notable that GloVe achieved a higher F-measure in inductive transfer learning than traditional learning. Our interpretation is that adding news training data to the biomedical tasks resulted in a higher performance with GloVe embeddings. This could correlate with the original training data of the GloVe model used in our experiments. Thus, our results show that using a small percentage from the target task while training our neural network results in a proximate outcome when compared to traditional learning.