Native Language Identification With Classifier Stacking and Ensembles

Shervin Malmasi, Mark Dras


Abstract
Ensemble methods using multiple classifiers have proven to be among the most successful approaches 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 several large data sets, evaluated in both intra-corpus and cross-corpus modes.
Anthology ID:
J18-3003
Volume:
Computational Linguistics, Volume 44, Issue 3 - September 2018
Month:
September
Year:
2018
Address:
Venue:
CL
SIG:
Publisher:
Note:
Pages:
403–446
Language:
URL:
https://www.aclweb.org/anthology/J18-3003
DOI:
10.1162/coli_a_00323
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PDF:
http://aclanthology.lst.uni-saarland.de/J18-3003.pdf