Sentence Order Prediction

Sentence Order Prediction (SOP) is a pre-training objective introduced in ALBERT to replace Next Sentence Prediction (NSP) — instead of predicting if two sentences are random or consecutive (topic matching), SOP makes the model predict which of two *consecutive* sentences came first, forcing it to learn coherence rather than just topic similarity.

SOP vs. NSP

• NSP Weakness: Negative examples are random sentences — easy to distinguish by topic (e.g., "Hockey" vs. "Cooking").
• SOP Hardness: Negative examples are the SAME two consecutive sentences but SWAPPED order (B then A).
• Task: Given two segments, predict if order is (A, B) or (B, A).
• Focus: Modeling coherence, logical flow, and discourse markers — mere topic matching is insufficient.

Why It Matters

• Better Representations: ALBERT showed SOP leads to significantly better performance on multi-sentence reasoning tasks (SQuAD, RACE, MNLI).
• Efficiency: Harder task signal allows more efficient learning of inter-sentence relationships.
• Structure: Forces the model to understand narrative flow and causal links.

SOP is fixing NSP — a harder ordering task that forces the model to learn logical coherence instead of just topic matching.