# Can We Survive without Labelled Data in NLP? Transfer Learning for Open Information Extraction
## 6 Results and Evaluation
### Results of Transfering to Relation Extraction  
Equally, in order to establish a fair comparison in the following three experiments, we fixed the training set size to 2906 relation instances. Results of both transductive and inductive transfer learning were compared against the results achieved by traditional learning. Results are reported in Table 8.  
Relation Extraction Results Discussion  
Firstly, in transductive transfer learning, with 50 batches and 10 epochs, BERT was able to achieve an F-measure of 54.4%. Both XLNet and XLM-RoBERTa scored the same F-measure of 49.1%, which was nearly 4.6% higher than the F-measure achieved using GloVe. With inductive transfer learning, we found an improvement of 12.8% when compared to transductive learning also using a 4:1 ratio, with the OIE news dataset overtaking the higher ratio. Using XLM-RoBERTa, a 67.2% F-measure was attained when the network was trained on 15 epochs and the training dataset was divided into 100 batches. BERT and XLNet did not fall far behind XLM-RoBERTa, as they achieved F-measures of 66.3% and 65.4%, respectively. GloVe achieved the lowest F-measure of 59.9%. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Source Task (Train)** & **Target Task (Test)** & \multicolumn{2}{c}{**Word**} & \multicolumn{2}{c}{**Hyperparameters**} & \multicolumn{2}{c}{**Results (R–P–F)**} \\  & & & **Embeddings** & **(Batches–Epochs)** & & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} Transductive \\ Transfer \\ Learning (BM1) \\ \end{tabular} } & \multirow{2}{*}{OIE (news)} & GloVe & 100 & 5 & 68.2\% & 58.4\% & 62.9\% \\  & & & BERT & 50 & 10 & 72.4\% & 51.3\% & 60.0\% \\  & & & XLNet & 100 & 5 & 68.4\% & 58.3\% & 62.9\% \\  & & & & **XLM-RoBERTa** & **100** & **5** & **71.0\%** & **59.0\%** & **64.4\%** \\ \multirow{2}{*}{\begin{tabular}{c} Inductive \\ Transfer \\ Learning (BM2) \\ \end{tabular} } & \multirow{2}{*}{OIE (news) + OIE (bio-medical)} & GloVe & 100 & 15 & 69.8\% & 78.2\% & 73.7\% \\  & & & BERT & 100 & 5 & 71.9\% & 78.9\% & 75.2\% \\  & & & XLNet & 100 & 10 & 73.6\% & 82.9\% & 77.9\% \\  & & & & **XLM-RoBERTa** & **100** & **5** & **73.0\%** & **83.8\%** & **78.0\%** \\ \multirow{2}{*}{\
\begin{tabular}{c} Traditional \\ Learning \\ \end{tabular} } & \multirow{2}{*}{OIE (bio-medical)} & GloVe & 100 & 5 & 70.8\% & 71.7\% & 71.2\% \\  & & & BERT & 100 & 15 & 73.1\% & 85.9\% & 78.9\% \\ \multirow{2}{*}{
\begin{tabular}{c} Traditional \\ Learning \\ \end{tabular} } & \multirow{2}{*}{OIE (bio-medical)} & \multirow{2}{*}{OIE (bio-medical)} & \multirow{2}{*}{OIE (bio-medical)} & \multirow{2}{*}{XLM-Net} & 100 & 15 & 72.9\% & 84.2\% & 78.1\% \\  & & & & **XLM-RoBERTa** & **100** & **15** & **72.5\%** & **86.9\%** & **79.0\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Domain adaptation results by transferring from the OIE news domain to the OIE bio-medical domain using four different word embeddings. Bold values indicate the highest achieved F-measure in each of the three experiments (transductive transfer learning, inductive transfer learning, traditional learning). When employing default learning settings, where we train on our target task, there was a 3.8% enhancement in the F-measure. Once again, BERT outperformed by scoring an F-measure of 71%, only 0.5% higher than XLNet, and 2.6% higher than XLM-RoBERTa. Consistently, GloVe scored the lowest F-measure of 65.9%, hereby proving the notable effect in the model's performance when using contextualized word embeddings in contrast with traditional word embeddings. Table 9 summarizes the best results of the three main experiments acquired in our work: ROIE model, transferring to bio-medical domain, and transferring to RE. As seen in Table 9, we could not single out a particular contextualized word embedding to utilize, as the use of word embedding may vary according to the various reasons: type of task (OIE, RE, or sentiment analysis), dataset domain (news, bio-medical data, or financial data), and the computational power available to the user.