STA: Self-controlled Text Augmentation for Improving Text Classifications

Despite recent advancements in Machine Learning, many tasks still involve working in low-data regimes which can make solving natural language problems difficult. Recently, a number of text augmentation techniques have emerged in the field of Natural Language Processing (NLP) which can enrich the training data with new examples, though they are not without their caveats. For instance, simple rule-based heuristic methods are effective, but lack variation in semantic content and syntactic structure with respect to the original text. On the other hand, more complex deep learning approaches can cause extreme shifts in the intrinsic meaning of the text and introduce unwanted noise into the training data. To more reliably control the quality of the augmented examples, we introduce a state-of-the-art approach for Self-Controlled Text Augmentation (STA). Our approach tightly controls the generation process by introducing a self-checking procedure to ensure that generated examples retain the semantic content of the original text. Experimental results on multiple benchmarking datasets demonstrate that STA substantially outperforms existing state-of-the-art techniques, whilst qualitative analysis reveals that the generated examples are both lexically diverse and semantically reliable.

Quantitative Metrics for Evaluating Explanations of Video DeepFake Detectors

The proliferation of DeepFake technology is a rising challenge in today's society, owing to more powerful and accessible generation methods. To counter this, the research community has developed detectors of ever-increasing accuracy. However, the ability to explain the decisions of such models to users is lacking behind and is considered an accessory in large-scale benchmarks, despite being a crucial requirement for the correct deployment of automated tools for content moderation. We attribute the issue to the reliance on qualitative comparisons and the lack of established metrics. We describe a simple set of metrics to evaluate the visual quality and informativeness of explanations of video DeepFake classifiers from a human-centric perspective. With these metrics, we compare common approaches to improve explanation quality and discuss their effect on both classification and explanation performance on the recent DFDC and DFD datasets.