@InProceedings{elfardy-EtAl:2017:I17-4,
  author    = {Elfardy, Heba  and  Srivastava, Manisha  and  Xiao, Wei  and  Kramer, Jared  and  Agarwal, Tarun},
  title     = {Bingo at IJCNLP-2017 Task 4: Augmenting Data using Machine Translation for Cross-linguistic Customer Feedback Classification},
  booktitle = {Proceedings of the IJCNLP 2017, Shared Tasks},
  month     = {December},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {59--66},
  abstract  = {The ability to automatically and accurately process customer feedback is a
	necessity in the private sector. Unfortunately, customer feedback can be one of
	the most difficult types of data to work with due to the sheer volume and
	variety of services, products, languages, and cultures that comprise the
	customer experience. In order to address this issue, our team built a suite of
	classifiers trained on a four-language, multi-label corpus released as part of
	the shared task on "Customer Feedback Analysis" at IJCNLP 2017. In addition to
	standard text preprocessing, we translated each dataset into each other
	language to increase the size of the training datasets. Additionally, we also
	used word embeddings in our feature engineering step. Ultimately, we trained
	classifiers using Logistic Regression, Random Forest, and Long Short-Term
	Memory (LSTM) Recurrent Neural Networks. Overall, we achieved a Macro-Average
	F-score between 48.7% and 56.0% for the four languages and ranked 3/12 for
	English, 3/7 for Spanish, 1/8 for French, and 2/7 for Japanese.},
  url       = {http://www.aclweb.org/anthology/I17-4009}
}