👉 Comment computing, also known as comment-based or meta-comment approaches, is a technique in natural language processing (NLP) where the structure of a text document is augmented with additional comments or annotations that provide context and structure to the main content. These comments can take various forms, such as tags, labels, or structured metadata, and are used to guide models in understanding the document's intent, relationships between different parts of the text, and the overall context. By explicitly marking key elements like entities, relationships, or segments of interest, comment computing helps models to better parse and generate human-like responses, especially in tasks like summarization, question answering, and dialogue systems. This approach enhances the model's ability to capture nuanced information and maintain coherence, leading to more accurate and contextually relevant outputs.
