# PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource Languages
## 4 Results and Discussion
### Experimental Results  
In Table 6, we present our experimental results with baseline models in two settings: single target training and sequential training. As our goal is to not only provide a state-of-the-art system but also to investigate the relationship between these targets, in sequential training we continued training individual target models on the next target by only resetting the prediction head layer to match the respective target. We experimented with all possible order combinations of the tasks and present only the results of the best performing order \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Parameter** & Batch size & Epochs & Max length & Learning rate & Dropout \\ \hline
**Range** & 4–64 & 1–20 & 64–512 & 1\(\times\)10\({}^{-6}\)–5\(\times\)10\({}^{-5}\) & 0.1–0.3 \\ \hline \multirow{2}{*}{**Parameter**} & warm-up & loss & \multirow{2}{*}{encoders} & \multirow{2}{*}{attention heads} & \\  & steps & multipliers & & & \\ \hline
**Range** & 10–200 & 0–1 & 1–12 & 4–12 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental parameters optimization search range which is represented by Irony, Hate Speech, Emotion, and Sentiment. In all experiments presented in Table 6, we used Greek BERT in order to maintain a level of similarity with past works. We tested several traditional machine learning algorithms, such as naive Bayes, decision tree, random forest, logistic regression, and support vector machine, as baseline models. To align with previous works, we also implemented NN models that incorporated a language model with an MLP layer for classification (Section 2). However, our results showed that even the NN-based baseline approaches outperformed the traditional machine learning algorithms, and using a transformer-based encoder layer instead of an MLP layer significantly improved the performance across all target classes compared to the NN-based baselines. This is due to the higher-level latent representations produced by the additional layer before the prediction stage. Our sequential models did not improve in performance in most cases, with the exception of the emotion class, when sentiment is the direct parent task. While this does indicate towards a possible connection among the target classes, this naive approach is not capable of taking advantage of the task relevance. Table 7 details our results with our proposed joint multi-task learning approach, with parameter sharing among the target heads. We showcase results with both Greek BERT and our domain-adapted version. In the equal contribution of each target class to the model's training setup, we notice an increase in performance in irony and emotion when using our domain-adapted model, while sentiment suffers in comparison and hate speech remains constant. The performance is either equal or worse when using Greek BERT for word representations, which suggests that DAGB contributes to this performance increase. In the scaled loss (LMP) setting, however, sentiment improves dramatically, along with emotion, while irony experiences a slight performance decrease and hate speech maintains the same level of performance. Taking all scores into account, we notice that LMP-based