- Zhisong Zhang, Emma Strubell, E. Hovy. 2023. On the Interactions of Structural Constraints and Data Resources for Structured Prediction. Abstract: In this work, we provide an analysis on the interactions of the effectiveness of decoding with structural constraints and the amount of available training data for structured prediction tasks in NLP. Our exploration adopts a simple protocol that enforces constraints upon constraint-agnostic local models at testing time. With evaluations on three typical structured prediction tasks (named entity recognition, dependency parsing, and event argument extraction), we find that models trained with less data predict outputs with more structural violations in greedy decoding mode. Incorporating constraints provides consistent performance improvements and such benefits are larger in lower resource scenarios. Moreover, there are similar patterns with regard to the model sizes and more efficient models tend to enjoy more benefits. Finally, we also investigate settings with genre transfer and discover patterns that are related to domain discrepancies.
