Large datasets on natural language inference are a potentially valuable resource for inducing semantic representations of natural language sentences. But in many such models the embeddings computed by the sentence encoder goes through an MLP-based interaction layer before predicting its label, and thus some of the information about textual entailment is encoded in the interpretation of sentence embeddings given by this parameterised MLP. In this work we propose a simple interaction layer based on predefined entailment and contradiction scores applied directly to the sentence embeddings. This parameter-free interaction model achieves results on natural language inference competitive with MLP-based models, demonstrating that the trained sentence embeddings directly represent the information needed for textual entailment. The inductive bias of this model leads to better generalisation to other natural language inference datasets, and the resulting sentence embeddings are better for other semantic tasks.
***Update***
I'd like to thank the authors for responding to my questions and for the additional experiments. I think the new sentence embedding experiments make the paper quite a bit stronger - it would be interesting to scale them up to using SNLI + MNLI to see how much further they can go (right now they are still below Sentence-BERT which also was trained on SNLI in addition to MNLI). I think this paper is an interesting idea, and my only other main concern is the transfer results to other NLI datasets. I think it would be a good idea to confirm that the difference between the two approaches is due to biases perhaps though an error analysis. I am borderline on this paper, but I feel enough improvement has been made to raise my rating.
This paper proposes a new way to train sentence embedding models using NLI data such that very few parameters are used for classification. This is in contrast to prior work where an MLP is used. They define an interaction layer where operations are applied to the sentence embeddings to produce just 5 scores, which are then fed into a softmax layer for the final prediction.
The reason for their approach is they hypothesize that the the classification layers are encoding some of the information, presumably lessening the amount of information in the sentence embeddings and also preventing them from having a direct interpretation.
Their model does surprisingly well on the entailment datasets, only about 1-1.5 points or so worse than using a MLP, and so they indeed demonstrate that the sentence embeddings contain a lot of entailment information. However, I do have some concerns about their claims that this helps in transfer learning to other NLI tasks. Their main results show transfer performance comparing their approach and using an MLP, but it seems that overall, on all datasets, their approach transfers more poorly. Two datasets that they do worse on they try to discount for either having terrible, below random performance (which is true) and the other for having the same biases as MNLI. However, if that was the case, I don't see why their model would perform worse since both theirs and the baseline would benefit from having these biases, but their model performs about 11 points less.
So therefore, I don't find the transfer experiments convincing, though it is interesting how different the models do on some of these tasks - model performance is surprisingly task dependent 
What I propose is for the authors to investigate if their sentence embeddings are in fact noticeably different than the ones trained in the more conventional matter. They could evaluate on sentence embedding and probing tasks (like SentEval) and see how the two models compare. It would be interesting to see wha encoded information differs between the models.
Im summary, I think this is an interesting experiment and it's nice to see that the MLP isn't doing a lot of heavy lifting (which also might be slightly counter to their hypothesis about the MLP containing a lot of entailment information). However, I find the transfer experiments unconvincing and the paper is short on analysis about when their model does better on transfer and when having an MLP helps, or how the learned sentence embeddings of the two models differ.