Text Augmentation Techniques in Drug Adverse Effect Detection Task (2021)

Abstract: The paper researches the problem of drug adverse effect detection in texts of social media. We describe the development of such classification system for Russian tweets. To increase the train dataset we apply a couple of augmentation techniques and analyze their effect in comparison with similar systems presented at 2021 years’ SMM4H Workshop.

## introduction attention-based neural network models significantly move forward performance frontier for a range of natural language processing (nlp) tasks. pre-trained models with transformer architectures @xcite essentially changed the way itself of approaching an nlp problem. fine-tuning such models for a specific task typically yields a solid result. but there are still challenging problems even among the simplest binary text classification tasks. for example, for current state-of-the-art nlp methods, it is not an easy task to differentiate drug adverse effects (ae) mentions among real indications for use. especially if the target text comes from informal data sources (see example in section 2). for several recent years, this problem stays in research focus and is offered as a shared task during the annual social media mining for health applications (smm4h) workshop @xcite . and the second time it was proposed for the russian language. the training data size can be crucial for deep learning algorithms generalization hence the performance metrics @xcite . this study explores the ways of gaining additional train data. we describe a couple of such techniques (translation and generation) and apply them to increase the training dataset more than 9 times. ## system description data augmentation techniques are well presented in the computer vision field @xcite . distortion of an input image allows getting an additional data sample. unfortunately for nlp tasks, there are no simple and effective operations to mine new data samples. mere word order change or replacement of words often leads to loss or change of text meaning. that is because natural language obeys numerous rules and restrictions. to account for most of these rules and 'correctly' transform a text one needs to rely on a language model. ## results and conclusions f 1 -score toward the positive class @xcite is the main evaluation metric for this task.