text: In the Following, pros and cons of the paper are presented. Pros ------- 1. Many real-world applications. 2. Simple architecture and can be reproduced (if given enough details.) Cons----------------------1. Ablation study showing whether bidirectional LSTM contributing to the similarity would be helpful. 2. Baseline is not strong.  How about using just LSTM? 4. It is suprising to see that only concatenation with MLP is used for optimization of capturing regularities across languages.  5. Equation-11 looks like softplus function more than vanilla ReLU. 6. How are the similarity assessments made in the gold standard dataset.  The cost function used only suggest binary assessments.  Please refer to some SemEval tasks for cross-lingual or cross-level assessments.  As binary assessments may not be a right measure to compare articles of two different lengths or languages. Minor issues------------1. SNS is meant to be social networking sites? 2. In Section 2.2, it denote that 'as the figure demonstrates'.  No reference to the figure. 3. In Section 3, 'discussed in detail' pointed to Section 2.1 related work section.  Not clear what is discussed in detail there. 4. Reference to Google Translate API is wrong. The paper requires more experimental analysis to judge the significance of the approach presented.