# Can We Survive without Labelled Data in NLP? Transfer Learning for Open Information Extraction
## 6 Results and Evaluation
### Results of Transfering to Relation Extraction
This is also in agreement with other papers that extensively compared embeddings in various tasks and found that the most suitable one is highly dependent on the task and data nature [51; 52]. To further elaborate that the choice of the word embedding is dependent upon the task and nature of data, XLNet outperformed all the other word embeddings when training and testing on the news dataset. However, on bio-Medical data, XLM-RoBERTa performed better in all three experiments: transductive transfer learning, inductive transfer learning, and traditional learning. It is worth noting that XLM-RoBERTa outperformed in four out of a total seven experiments in our work. Thus, we were motivated to compare and experiment with the use of different word embeddings. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Source Task (Train)** & **Target Task (Test)** & \
\begin{tabular}{c} **Word** \\ **Embeddings** \\ \end{tabular} &
\begin{tabular}{c} **Hyperparameters** \\ **(Batches-Epochs)** \\ \end{tabular} & \multicolumn{3}{c}{**Results (R–P–P)**} \\ \hline Transductive & & & GloVe & 100 & 5 & 55.9\% & 37.0\% & 44.5\% \\ Transfer & OIE (news) & RE (news) & **BERT** & **50** & **10** & **62.2\%** & **48.4\%** & **54.4\%** \\  & & & XLNet & 50 & 5 & 58.8\% & 42.1\% & 49.1\% \\  & & & XLM-RoBERTa & 100 & 15 & 53.2\% & 45.6\% & 49.1\% \\ Inductive & & & GloVe & 100 & 10 & 52.8\% & 69.3\% & 59.9\% \\ Transfer & OIE (news) + RE & RE (news) & **BERT** & 100 & 5 & 61.7\% & 73.0\% & 66.3\% \\ Learning (RE2) & & & XLNet & 100 & 15 & 59.7\% & 72.2\% & 65.4\% \\  & & & **XLM-RoBERTa** & **100** & **15** & **59.7\%** & **76.9\%** & **67.2\%** \\  & & & GloVe & 100 & 15 & 57.6\% & 77.1\% & 65.9\% \\ Traditional & RE (news) & RE (news) & **BERT** & **100** & **15** & **62.4\%** & **82.3\%** & **71.0\%** \\ Learning & & & XLNet & 100 & 5 & 61.6\% & 81.3\% & 70.5\% \\  & & & XLM-RoBERTa & 100 & 15 & 59.8\% & 79.9\% & 68.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of transferring from OIE to RE 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). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Source Task (Train)**} & \multirow{2}{*}{**Target Task (Test)**} & \multirow{2}{*}{
\begin{tabular}{c} **Word** \\ **Embeddings** \\ \end{tabular} } & \multicolumn{3}{c}{**Hyperparameters**} & \multicolumn{3}{c}{**Results (R–P–P)**} \\ \hline OIE (news) & OIE (news) & XLNet & 100 & 20 & 68.1\% & 62.2\% & 65.0\% \\ \hline OIE (news) & OIE (bio-medical) & XLM-RoBERTa & 100 & 5 & 71.0\% & 59.0\% & 64.4\% \\ \hline OIE (news) + OIE (bio-medical) & OIE (bio-medical) & XLM-RoBERTa & 100 & 5 & 73.0\% & 83.8\% & 78.0\% \\ \hline OIE (bio-medical) & OIE (bio-medical) & XLM-RoBERTa & 100 & 15 & 72.5\% & 86.9\% & 79.0\% \\ \hline OIE (news) & RE (news) & BERT & 50 & 10 & 62.2\% & 48.4\% & 54.4\% \\ \hline OIE (news) + RE (news) & RE (news) & XLM-RoBERTa & 100 & 15 & 59.7\% & 76.9\% & 67.2\% \\ \hline RE (news) & RE (news) & BERT & 100 & 15 & 62.4\% & 82.3\% & 71.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of the best result obtained in each experiment by different systems described in the paper: original ROIE model, transferring from OIE to bio-medical OIE (transductive transfer learning, inductive transfer learning, traditional learning), and transferring from OIE to (transductive transfer learning, inductive transfer learning, inductive transfer learning, traditional learning).