Complex Structure Leads to Overfitting:A Structure Regularization Decoding Method for Natural Language Processing
Table 4: UASo of different individual models on test data. The upper and bottom blocks present results obtained by sibling and tri-sibling models respectively. We cannot draw a conclusion whether the complex structure models performs better or not, because the results vary with the structural complexity and the data. This is expected, because structural complexity affects both the empirical risks and the structural risks. As the data (English vs. Chinese) and the structure (Sibling vs. Tri-sibling) are different, the relations between empirical risks and the structural risks are also different. Hence, the varied results.
['Factorization', 'Empty Element', 'English', 'Chinese']
We focus on the prediction for overt words only. When we take into account empty categories, more information is available. However, the increased structural complexity affects the algorithms. From the table, we can see that the complex sibling factorization works worse than the simple sibling factorization in English, but works better in Chinese. The results of the tri-sibling factorization are exactly the opposite. The complex tri-sibling factorization works better than the simple tri-sibling factorization in English, but works worse in Chinese. The results can be explained by our theoretical analysis. While the structural complexity is positively correlated with the overfitting risk, it is negatively correlated with the empirical risk. In this task, although the overfitting risk is increased when using the complex structure, the empirical risk is decreased more sometimes. Hence, the results vary both with the structural complexity and the data.