Easy First, Not Best Overall
TL;DR for operators A team choosing a sequence-labeling model with limited annotated data should compare both prediction accuracy and the cost of producing it. With only 10% of English CCG Rebank training data, TagInsert achieves 90.99% accuracy against 86.78% for a left-to-right Transformer, but BERT still leads at 92.35%. TagInsert’s confidence-guided insertion strategy makes the largest difference against conventional autoregressive decoding when labeled data are scarce. ...