Complex Structure Leads to Overfitting:A Structure Regularization Decoding Method for Natural Language Processing
Table 5: UASo of different SR decoding models on test data. As we can see, SR decoding consistently improves the complex structure models. All improvements are statistically significant.
['Method', 'English', 'Chinese']
We can see that the SR decoding framework is effective to deal with the structure-based overfitting. This time, the accuracy of analysis for overt words is consistently improved. For the second-order model, SR decoding reduces the error rate by 3.1% for English, and by 3.0% for Chinese. For the third-order model, the error rate reduction of 4.0% for English, and 5.5% for Chinese is achieved by the proposed method. Similar to the sequence labeling tasks, the third-order model is improved more. We suppose the consistent improvements come from the ability of reducing the structural risk of the SR decoding algorithm. Although in this task, the complex structure is sometimes helpful to the accuracy of the parsing, the structural risk still increases. By regularizing the structural complexity, further improvements can be achieved, on top of the decreased empirical risk brought by the complex structure.