text: Overall strength: In this paper, the authors proposed target-aware memory networks to model sentiment interactions between target aspects and the context words with attentions.  This work has a well-established motivation:  traditional attention for target-dependent sentiment classification cannot model the interaction between target term and context words when making predictions.  To solve this problem, the authors proposed five formulations in the final prediction layer.  The illustration about the problem is clear, as well as the explanation for the formulations. Major concerns: 1.	This work brings some modifications to the prediction layer,  which is a bit trivial.  Although the effect has been shown,  the model is too specific to a narrow area, and is not general to be applied in a broad sense.  It could have more contribution if the authors model the interactions within the attention model itself, instead of a simple prediction layer, which is problem-dependent. 2.	The experiments are insufficient to show the effectiveness.  It would be better to provide some statistics showing how the target-context interaction model outperforms the traditional ones in the special cases like the one shown in Table 4.  Only two examples are not convincing. 3.	In section 3, the authors claimed that (5) models the target and context independently.  However, in section 4, in (7), the authors claimed the target vector v_t will affect the context shifting their representation to c’_i.  This should also work for (5).  4.	There are too many typos in the paper, e.g., \alpha is replaced by a, etc. Other concerns: 1.	It seems that one needs to train at least three embedding matrices: A, C, D which represent input embeddings, output embeddings, and interactive embeddings, respectively.  I wonder if this brings redundant parameters that do not guarantee convergence.  Why not use one matrix instead?  Did the authors try experiments with less embedding matrices? 2.	There is another work that also considers the target-context interaction using interactive attention model.  Please refer to this paper “Interactive Attention Networks for Aspect-Level Sentiment Classification”.  A comparison is needed. 3.	It is better to provide results in terms of accuracy for both datasets,  as previous methods usually use accuracy for comparison.  How’s the score of the proposed model compared with the above paper as well as [Tang et al. 2016]?