Venue: ICLR 2017 conference

Reviews:

final_score: rating: null
review_content: Have you considered evaluating on the SNLI dataset as well? This dataset is broader than SICK in certain ways, and might be more challenging. I am curious to see if the summation-based method presented here can do as well as the attention-based systems.
Having said that, the evaluation is quite comprehensive as is!
review_title: Performance on the SNLI dataset

final_score: rating: 7: Good paper, accept
review_content: This is a good paper with an interesting probabilistic motivation for weighted bag of words models.
The (hopefully soon) added comparison to Wang and Manning will make it stronger. 
Though it is sad that for sufficiently large datasets, NB-SVM still works better.

In the second to last paragraph of the introduction you describe a problem of large cooccurrence counts which was already fixed by the Glove embeddings with their weighting function f.

Minor comments:

"The capturing the similarities" -- typo in line 2 of intro.
"Recently, (Wieting et al.,2016) learned" -- use citet instead of parenthesized citation
 
review_title: Accept

final_score: rating: null
review_content: In the experiments, did the sentence set S mentioned in algorithm 1 include both training and test examples? If the test examples were not included, then how to do the projection step in line 6 on the test examples?
review_title: Experimental setup

final_score: rating: null
review_content: It'd be great to compare on some of the dataset used in Wang and Manning who use simple Naive Bayes SVM hybrids.
For example: IMDB or Stanford Sentiment Treebank.
How does your model do on those?
review_title: Comparison

final_score: rating: 7: Good paper, accept
review_content: This paper proposes a simple way to reweight the word embedding in the simple composition function for sentence representation. This paper also shows the connection between this new weighting scheme and some previous work.

Here are some comments on technical details:

- The word "discourse" is confusing. I am not sure whether the words "discourse" in "discourse vector c_s" and the one in "most frequent discourse" have the same meaning.
- Is there any justification about $c_0$ related to syntac?
- Not sure what thie line means: "In fact the new model was discovered by our detecting the common component c0 in existing embeddings." in section "Computing the sentence embedding"
- Is there any explanation about the results on sentiment in Table 2?
review_title: Interesting model and analysis

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: This paper presents a new theoretically-principled method of representing sentences as vectors. The experiments show that vectors produced by this method perform well on similarity and entailment benchmarks, surpassing some RNN-based methods too.

Overall, this is an interesting empirical result, especially since the model is not order-sensitive (as far as I can tell). I would like to see some more discussion on why such a simple model does better than LSTMs at capturing similarity and entailment. Could this be an artifact of these benchmarks?
review_title: Accept

Meta-review: A new method for sentence embedding that is simple and performs well. Important contribution that will attract attention and help move the field forward.
Decision: Accept (Poster)