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.

翻译：本文描述我们的IJCNLP 2017 客户反馈分析共同任务系统。 我们实验了简单的神经结构, 在某些任务上具有竞争性性能。 其中包括浅度CNN 和双向LSTM 结构, Facebook的快速文本为基线模型。 我们的最佳模式是使用西班牙文( 两者均为85.28%)和法文(分别为70%和73.17 % ) 的精确度和微速- F1 衡量标准在前5 系统中, 并且超过了所有其他评论模型(87.28%)和无意义的(51.85%)标记, 使用微度平均F1, 标记用于法国任务。