# On the Use of Knowledge Transfer Techniques for Biomedical Named Entity Recognition +
## 4 Results and Discussion of MTM-CNN
### Effects of Different Auxiliary Tasks  
In the previous experiments, all the auxiliary tasks were the same, i.e., both auxiliary and main tasks were the same. In order to see the effect of different tasks in the MTL approach, the experiments were extended with various tasks, also considering the same BioNER task but at a different level of layers in the MTM-CNN. In this regard, three different approaches were adopted. In the first approach (MTM-CNN\({}^{\bigstar}\)), during the training of the MTM-CNN, two additional but different auxiliary tasks were introduced: these were GENIA-POS tagging and CoNLL chunking. In the second approach, MTM-CNN\({}^{\bigstar}\)\({}^{in}\), the auxiliary tasks (GENIA-POS tagging and CoNLL chunking) were trained at the innermost layer. This eliminated the task-specific BiLSTM (layer denoted by a circled 3 in Figure 3) for these two auxiliary tasks. Using auxiliary tasks at the innermost layer helped the outermost layer (circled 3) to learn from a complete representation of the auxiliary tasks. The presented approach illustrated the performance \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Datasets** & **[PERSON] et al. [35]** & **[PERSON] et al. [34]** & **STM-CNN** \\ \hline AnatEM & 85.3 & 81.5 & **85.8** \\ BC2 GM & 80.0 & 72.6 & **80.9** \\ BC4 CHEMD & **88.7** & 82.9 & 88.6 \\ BC5 CDR & **86.9** & 83.6 & 85.6 \\ BioNLP09 & 84.2 & 83.9 & **87.0** \\ BioNLP11 EPI & 77.6 & 77.7 & **81.4** \\ BioNLP11 ID & 74.6 & 81.5 & **83.2** \\ BioNLP13 CG & **81.8** & 76.7 & 81.2 \\ BioNLP13 GE & 69.3 & 73.2 & **73.3** \\ BioNLP13 PC & 85.4 & 80.6 & 86.3 \\ CRAFT & 81.2 & 79.5 & **83.8** \\ Ex-PTM & 67.6 & 68.5 & **72.7** \\ JNLPBA & 72.1 & 69.6 & **74.4** \\ LINNAEUS & 86.9 & 83.9 & **87.3** \\ NCBI-disease & 83.9 & 80.2 & **84.1** \\ Average & 80.4 & 78.4 & **82.4** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Single-task model results comparison. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Datasets** & **[PERSON] et al. [35]** & **[PERSON] et al. [34]** & **MTM-CNN** \\ \hline AnatEM & 86.0 & 82.2 & **87.0** \\ BC2 GM & 78.9 & 73.2 & **80.8** \\ BC4 CHEMD & **88.8** & 83.0 & 87.4 \\ BC5 CDR & **88.1** & 83.9 & 87.9 \\ BioNLP09 & 88.1 & 84.2 & **88.7** \\ BioNLP11 EPI & 83.2 & 78.9 & **84.8** \\ BioNLP11 ID & 83.3 & 81.7 & **87.7** \\ BioNLP13 CG & 82.5 & 78.9 & **84.3** \\ BioNLP13 GE & **79.9** & 78.6 & 79.8 \\ BioNLP13 PC & 88.5 & 81.9 & **88.8** \\ CRAFT & 82.9 & 79.6 & **83.2** \\ Ex-PTM & 80.2 & 74.9 & **81.0** \\ JNLPBA & 72.2 & 70.0 & **74.1** \\ LINNAEUS & **88.9** & 84.0 & 87.8 \\ NCBI-disease & 85.5 & 80.4 & **85.7** \\ Average & 83.8 & 79.7 & **84.6** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results comparison for different multitask models. improvement compared to the first approach, which therefore motivated the third approach of auxiliary tasks of this section. In the third approach (MTM-CNN\({}^{in}\)), the auxiliary tasks were the same BioNER tasks used in the MTM-CNN but this time, the auxiliary tasks were trained at the innermost layer (without task-specific BiLSTM) and the main task was used at the outermost layer (having a shared BiLSTM layer, a connection from the circled 2 to the circled 4 in Figure 3)). For instance, the MTM-CNN\({}^{in}\) was trained for AnatEM, then the rest of the datasets (BC2 GM, BC4 CHEMD, etc.) were treated as auxiliary tasks for AnatEM and did not have any task-specific BiLSTM. More specifically, in both the second and third approaches, the auxiliary tasks did not have the task-specific BiLSTM layer, while the main tasks had the task-specific BiLSTM layer. Table 4 shows the results of approaches using different auxiliary tasks. It can be seen that by introducing the GENIA-POS and CoNLL chunking auxiliary tasks, the results (MTM-CNN\({}^{\clubsuit}\)) improved for ten datasets against the MTM-CNN with an F1-score gain of up to 2.5%. We see that the MTM-CNN\({}^{\clubsuit}\) improved the F1-score for CRAFT from 80.9 to 83.6.