Research on data generation and augmentation has been focused majorly around enhancing generation models, leaving a notable gap in the exploration and refinement of methods for evaluating synthetic data. There are several text similarity metrics within the context of generated data filtering which can impact the performance of specific Natural Language Understanding (NLU) tasks, specifically focusing on intent and sentiment classification. In this study, we propose RankAug, a text-ranking approach that detects and filters out the top augmented texts in terms of being most similar in meaning with lexical and syntactical diversity. Through experiments conducted on multiple datasets, we demonstrate that the judicious selection of filtering techniques can yield a substantial improvement of up to 35% in classification accuracy for under-represented classes.

Privacy is of primary importance when it comes to the Financial Domain as the data is highly confidential and no third party can be having access to it. Natural Language Processing (NLP) techniques can be applied for text classification and entity detection purposes in financial domains like customer feedback sentiment analysis, invoice entity detection, categorisation of financial documents by type etc. Due to the sensitive nature of such data, privacy measures need to be taken for handling and training large models with such data. In this work, we propose a contextualized transformer (BERT and RoBERTa) based text classification model integrated with privacy features like Differential Privacy (DP) and Federated Learning (FL). We present how to privately train NLP models and desirable privacy utility trade-offs and evaluate it on the Financial Phrase Bank dataset.