# Enhancing Fake News Detection in Romanian Using Transformer-Based Back Translation Augmentation
## 3 Methodology
### Augmentation  
BT is a powerful technique for augmenting text data. This technique has been widely used and has been shown to be beneficial in enhancing the performance of different NLP assignments, including the detection of fake news [10]. BT generates new instances of text data that convey a similar meaning, but with different phrasing, by translating it into another language and then back [24]. This approach introduces diversity in training data, exposing fake news detection models to a wider spectrum of linguistic patterns and expressions [6]. BT augmentation is particularly valuable when labeled data are limited or unavailable to train fake news detection models [24]. By generating additional data instances, BT effectively increases the size of the training set. This augmentation technique improves the model's capability to generalize and capture the underlying patterns in fake news, leading to enhanced classification performance [25]. To provide data augmentation in our fake news detection experiments, we selected the Factual.ro data set as base data. This data set contains labeled instances from Romanian online news sources, accurately identified by independent fact-checkers to differentiate between real and fake news. To train the models and avoid overfitting, we partitioned the original data set into training and test sets with a split of 80-20. The test set remained unseen during training, allowing us to assess the model's generalization capability. We applied the BT augmentation technique exclusively to the training data set. Multilingual Denoising Autoregressive BART (mBART) is a powerful sequence-to-sequence model designed to remove noise and improve the quality of natural language text. Using the advanced BART (Bidirectional and Auto-Regressive Transformers) objective, this model is pre-trained on large-scale monolingual corpora from multiple languages [26]. It is a versatile multilingual model suitable for processing various languages and is specifically beneficial for machine translation tasks. One of the applications of mBART is to augment text data through BT. \begin{table}
\begin{tabular}{c l l} \hline \hline \# & **Attributes** & **Description** \\ \hline
1 & Topic & The category of the instance. This could include topics such as transport, work, politics, justice, and finance. \\
2 & Content & The main content of the statement. \\
3 & Date & The date the statement was published. \\
4 & Author Name & The name of the author of the statement. \\
5 & Affiliation & The author’s affiliation, such as their party, position, or organization. \\
6 & Source Name & The name of the news source where the statement was published. \\
7 & Context & Additional information or context provided in the statement. \\
8 & Label & A label indicating whether the instance is classified as fake or real news. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Factual.ro fake news data set. Using the new training data set, we implemented BT data augmentation using two distinct approaches: Google Translate and the mBART model. In each of these approaches, the training data set was translated into the target language and back to Romanian. We used two different languages as the target language, English and French. As a result, new sets of training data were obtained. By incorporating synthetic data, our fake news detection models were able to learn from a broader range of language patterns and improve their performance in detecting fake news in Romanian. Table 2 provides concrete examples of how machine translation services and transformer-based models augmented the Factual.ro data set using BT. Specifically, this table showcases the application of both Google Translate and mBART in BT. Initially, the table presents a Romanian example statement without any augmentation. Then, the table shows an instance of BT with Google Translate, illustrating both the process of translation to English and subsequent translation back to Romanian.