Task: Language Identification
Input (Spanish, English): - Strengths:  This is the first neural network-based approach to argumentation mining. The proposed method used a Pointer Network (PN) model with multi-task learning and outperformed previous methods in the experiments on two datasets.  - Weaknesses:  This is basically an application of PN to argumentation mining. Although the combination of PN and multi-task learning for this task is novel, its novelty is not enough for ACL long publication. The lack of qualitative analysis and error analysis is also a major concern.  - General Discussion:  Besides the weaknesses mentioned above, the use of PN is not well-motivated. Although three characteristics of PN were described in l.138-143, these are not a strong motivation against the use of bi-directional LSTMs and the attention mechanism. The authors should describe what problems are solved by PN and discuss in the experiments how much these problems are solved.  Figures 2 and 3 are difficult to understand. What are the self link to D1 and the links from D2 to E1 and D3/D4 to E2? These are just the outputs from the decoder and not links. The decoder LSTM does not have an input from e_j in these figures, but it does in Equation (3). Also, in Figure 3, the abbreviation "FC" is not defined.  Equation (8) is strange. To calculate the probability of each component type, the probability of E_i is calculated.  In the experiments, I did not understand why only "PN", which is not a joint model, was performed for the microtext corpus.  It is not clear whether the BLSTM model is trained with the joint-task objective.  There are some studies on discourse parsing using the attention mechanism. The authors should describe the differences from these studies.  Minor issues:  l.128: should related -> should be related  l.215: (2015) is floating  l.706: it able -> it is able  I raised my recommendation score after reading the convincing author responses. I strongly recommend that the authors should discuss improved examples by PN as well as the details of feature ablation.
Output (English): en