Sentence Level

Sentence-level analysis in natural language processing focuses on understanding and processing individual sentences within larger texts, aiming to improve various downstream tasks. Current research emphasizes developing robust sentence representations using techniques like multi-task learning, transformer architectures (e.g., RoBERTa), and incorporating both sentence- and token-level objectives to capture finer-grained information. This work is crucial for advancing applications such as machine translation, lexicography, and automated essay scoring, where accurate sentence-level understanding is essential for achieving high performance and improving human-computer interaction.