# PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource Languages
## 5 Conclusions  
In this paper, we present PIMA, an end-to-end intelligent media analytics framework for the classification of Greek text for four separate tasks: sentiment, irony, hate speech, and emotion classification. At the same time, we introduce a classification dataset that allows for the joint multi-task training on the four targets that was manually collected and annotated by experts. Our framework's model utilizes neural network parameter sharing to take advantage of the contextual similarities between the tasks in a joint fashion. We further extend our framework by domain adapting (aligning) the underlying language model to the media analytics domain with the introduction of a second dataset for this purpose. The joint learning approach to the multi-task model in our framework leverages the individual loss functions by applying multipliers to adjust the learning process. Consequently, our proposed framework achieves a state-of-the-art performance by employing minimal feature engineering and preprocessing, coupled with parallel classifications. This approach enables high-throughput, real-world deployment and application in many media-related business areas. In future work, we plan to extend both datasets for multi-task classification and language model adaption, making them publicly available to facilitate further research. This allows for training the underlying language model and other transformer-based LMs from scratch, potentially leading to improved NLP tools for the Greek language and low resource languages in general. Additionally, we also plan to explore other methods for improving our framework's performance by further investigating transfer learning techniques that allow us to leverage pre-trained language models from other languages. These methods have shown promising results in other domains and languages, and have the potential to significantly enhance the effectiveness and efficiency of NLP tools for low-resource languages in general. Conceptualization, D.Z. ; methodology, D.Z. and N.S. ; formal analysis, D.Z. and N.S. ; investigation, D.Z. and N.S. ; software, D.Z. and N.S. ; resources, D.Z. and N.S. ; validation, D.Z. and N.S. ; writing--original draft preparation, D.Z., N.S. and I.V. ; writing--review and editing, D.Z., N.S. and I.V. ; visualization, D.Z. ; supervision, N.S. and I.V. ; project administration, D.Z. and I.V. ; funding acquisition, D.Z., N.S. and I.V. All authors have read and agreed to the published version of the manuscript. This research was carried out as part of the project KMP6-0096055 under the framework of the Action \"Investment Plans of Innovation\" of the Operational Program \"Central Macedonia 2014-2020\", which is co-funded by the European Regional Development Fund and Greece. Informed Consent Statement: Not applicable. Data Availability Statement: The complete dataset (training, development, test splits, and balanced training set), along with the code to reproduce all the experiments are available at: [[https://github.com/d1](https://github.com/d1)]([https://github.com/d1](https://github.com/d1)) mitriz/pima (accessed on 1 March 2023). The domain-adapted Greek BERT model weights are available at: [[https://huggingface.co/dimitriz/greek-media-bert-base-uncased](https://huggingface.co/dimitriz/greek-media-bert-base-uncased)]([https://huggingface.co/dimitriz/greek-media-bert-base-uncased](https://huggingface.co/dimitriz/greek-media-bert-base-uncased)) (accessed on 1 March 2023). This research was supported by the NVIDIA Applied Research Accelerator Program (ARAP) as part of the grant Advance Document Repository Mining (ADoRe). The authors declare no conflict of interest. The following abbreviations are used in this manuscript: