Towards robust natural language and image processing In low-resource scenarios - Liu, Linlin (Nanyang Technological University, 2023)
Deep learning has achieved state-of-the-art performance on a wide range of tasks, including natural language processing (NLP), computer vision (CV), speech and so on. Compared with the traditional statistical model-based machine learning methods, it eliminates the reliance on tedious feature engineering work, and leverages neural models for automatic feature extraction. Neural models are data hungry, which usually requires a large amount of data to achieve the desired performance. When there is limited training data, the neural models are prone to overfitting. However, it is often expensive and time-consuming to annotate a large amount of data for training. Therefore, improving neural model robustness when there is limited data has been an increasingly important area in deep learning. In this thesis, we present our research on robust natural language and image processing methods in low-resource scenarios.