Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture
This work addresses the need for domain experts (e.g., doctors) to provide both labels and explanations in real-world tasks, though it is incremental as it builds on existing AL methods by adding explanation components.
The paper tackles the problem of active learning (AL) neglecting natural language explanations in low-resource scenarios by proposing a novel AL architecture that incorporates explanation generation and diversity-based sampling, resulting in improved human annotation efficiency and trustworthiness as demonstrated in evaluations.
Real-world domain experts (e.g., doctors) rarely annotate only a decision label in their day-to-day workflow without providing explanations. Yet, existing low-resource learning techniques, such as Active Learning (AL), that aim to support human annotators mostly focus on the label while neglecting the natural language explanation of a data point. This work proposes a novel AL architecture to support experts' real-world need for label and explanation annotations in low-resource scenarios. Our AL architecture leverages an explanation-generation model to produce explanations guided by human explanations, a prediction model that utilizes generated explanations toward prediction faithfully, and a novel data diversity-based AL sampling strategy that benefits from the explanation annotations. Automated and human evaluations demonstrate the effectiveness of incorporating explanations into AL sampling and the improved human annotation efficiency and trustworthiness with our AL architecture. Additional ablation studies illustrate the potential of our AL architecture for transfer learning, generalizability, and integration with large language models (LLMs). While LLMs exhibit exceptional explanation-generation capabilities for relatively simple tasks, their effectiveness in complex real-world tasks warrants further in-depth study.