@InProceedings{dhyani:2017:I17-4,
  author    = {Dhyani, Dushyanta},
  title     = {OhioState at IJCNLP-2017 Task 4: Exploring Neural Architectures for Multilingual Customer Feedback Analysis},
  booktitle = {Proceedings of the IJCNLP 2017, Shared Tasks},
  month     = {December},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {170--173},
  abstract  = {This paper describes our systems for IJCNLP 2017 Shared Task on Customer
	Feedback Analysis. We experimented with simple neural architectures that gave
	competitive performance on certain tasks. This includes shallow CNN and
	Bi-Directional LSTM architectures with Facebook’s Fasttext as a baseline
	model. Our best performing model was in the Top 5 systems using the
	Exact-Accuracy and Micro-Average-F1 metrics for the Spanish (85.28% for both)
	and French (70% and 73.17% respectively) task, and outperformed all the other
	models on comment (87.28%) and meaningless (51.85%) tags using Micro Average F1
	by Tags metric for the French task.},
  url       = {http://www.aclweb.org/anthology/I17-4028}
}