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In this paper, we propose a deep neural network based natural language processing system for semantic textual similarity prediction. We leverage multi-layer bidirectional LSTM to learn sentence representation. After that, we construct matching features followed by Highway Multilayer Perceptron to make predictions. Experimental results demonstrate that this approach can’t get better results on standard evaluation datasets.

UTA DLNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation