Native Language Identification using Stacked Generalization
This work addresses the need for more robust and statistically validated ensemble methods in NLI, which is important for applications in linguistics and language processing, though it is incremental in nature.
The paper tackled the problem of Native Language Identification (NLI) by systematically evaluating ensemble methods, including classifier stacking, and achieved state-of-the-art results on three datasets from different languages, with statistical significance testing showing significant improvements over previous methods.
Ensemble methods using multiple classifiers have proven to be the most successful approach for the task of Native Language Identification (NLI), achieving the current state of the art. However, a systematic examination of ensemble methods for NLI has yet to be conducted. Additionally, deeper ensemble architectures such as classifier stacking have not been closely evaluated. We present a set of experiments using three ensemble-based models, testing each with multiple configurations and algorithms. This includes a rigorous application of meta-classification models for NLI, achieving state-of-the-art results on three datasets from different languages. We also present the first use of statistical significance testing for comparing NLI systems, showing that our results are significantly better than the previous state of the art. We make available a collection of test set predictions to facilitate future statistical tests.