Incremental Sequence Labeling: A Tale of Two Shifts

Understand

The incremental sequence labeling task involves continuously learning new classes over time while retaining knowledge of the previous ones.

• Our investigation identifies two significant semantic shifts: E2O (where the model mislabels an old entity as a non-entity) and O2E (where the model labels a non-entity or old entity as a new entity).
• Previous research has predominantly focused on addressing the E2O problem, neglecting the O2E issue.
• This negligence results in a model bias towards classifying new data samples as belonging to the new class during the learning process.