Sequence Tagging

Sequence tagging is a natural language processing technique that assigns labels to individual elements within a sequence of text, such as words in a sentence, aiming to extract structured information like parts-of-speech, named entities, or grammatical errors. Current research emphasizes improving model performance through techniques like data augmentation, ensembling different transformer-based architectures (including encoder-decoder and encoder-only models), and exploring novel training objectives such as reward optimization. These advancements are driving progress in various applications, including propaganda detection, machine-generated text identification, and grammatical error correction, ultimately enhancing the accuracy and efficiency of information extraction from text.