@InProceedings{kepler-astudillo-abad:2017:BEA,
  author    = {Kepler, Fabio  and  Astudillo, Ram\'{o}n  and  Abad, Alberto},
  title     = {Fusion of Simple Models for Native Language Identification},
  booktitle = {Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {423--429},
  abstract  = {In this paper we describe the approaches we explored for the 2017 Native
	Language Identification shared task. We focused on simple word and sub-word
	units avoiding heavy use of hand-crafted features. Following recent trends, we
	explored linear and neural networks models to attempt to compensate for the
	lack of rich feature use. Initial efforts yielded f1-scores of 82.39% and
	83.77% in the development and test sets of the fusion track, and were
	officially submitted to the task as team L2F. After the task was closed, we
	carried on further experiments and relied on a late fusion strategy for
	combining our simple proposed approaches with modifications of the baselines
	provided by the task. As expected, the i-vectors based sub-system dominates the
	performance of the system combinations, and results in the major contributor to
	our achieved scores. Our best combined system achieves 90.1% and 90.2% f1-score
	in the development and test sets of the fusion track, respectively.},
  url       = {http://www.aclweb.org/anthology/W17-5048}
}