Span Answer

Span answer extraction focuses on identifying the specific text segments within a document that answer a given question, addressing the challenge of retrieving precise information from large text corpora. Current research emphasizes improving the accuracy and robustness of this process, particularly for complex questions requiring multiple answer spans or dealing with diverse question types and languages, often employing transformer-based models and techniques like contrastive learning and mixture-of-experts architectures. This work is crucial for advancing question answering systems across various domains, from healthcare and education to general-purpose information retrieval, ultimately enhancing the accessibility and reliability of information extraction from textual data.