text: This paper presents a very thorough empirical exploration of the qualities and limitations of very simple word-embedding based models.  Average and/or max pooling over word embeddings (which are initialized from pretrained embeddings) is used to obtain a fixed-length representation for natural language sequences, which is then fed through a single layer MLP classifier.  In many of the 9 evaluation tasks, this approach is found to match or outperform single-layer CNNs or RNNs. The varied findings are very clearly presented and helpfully summarized,  and for each task setting the authors perform an insightful analysis. My only criticism would be the fact that the study is limited to English,  even though the conclusions are explicitly scoped in light of this.  Moreover, I wonder how well the findings would hold in a setting with a more severe OOV problem than is perhaps present in the studied datasets. Besides concluding from the presented results that these SWEMs should be considered a strong baseline in future work,  one might also conclude that we need more challenging datasets! Minor things:- It wasn't entirely clear how the text matching tasks are encoded.  Are the two sequences combined into a single sequence before applying the model, or something else?  I might have missed this detail. - Given the two ways of using the Glove embeddings for initialization (direct update vs mapping them with an MLP into the task space), it would be helpful to know which one ended up being used (i.e. optimal) in each setting. - Something went wrong with the font size for the remainder of the text near Figure 1.