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This paper describes our systems for IJCNLP 2017 Shared Task on CustomerFeedback Analysis. We experimented with simple neural architectures that gavecompetitive performance on certain tasks. This includes shallow CNN andBi-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 andMicro-Average-F1 metrics for the Spanish (85.28% for both) and French (70% and73.17% respectively) task, and outperformed all the other models on comment(87.28%) and meaningless (51.85%) tags using Micro Average F1 by Tags metricfor the French task.

OhioState at IJCNLP-2017 Task 4: Exploring Neural Architectures for Multilingual Customer Feedback Analysis