Venue: ICLR 2018 Workshop

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: This paper proposes to extend the bi-directional LSTM (BiLSTM) for suffixes encoding. In sum, this paper is quite clear, although I would like to see more motivation for the proposed approach. In my opinion, the only difference of this model compared to the BiLSTM is that the author presents information encoded in each point of a sentence twice (left-to-right and right-to-left context). It means also that the number of parameters is doubled compared to a traditional BiLSTM. Therefore, in order to have a fair comparison, the author should double the number of parameters of the BiLSTM too (Table 1).

In Table 2, the results are mixed. The author should provide more intuition and insights to readers to have a better understanding why. Furthermore, I suggest the author to add a BiLSTM with self-attention as another baseline system. 

review_title: Minor contribution with mixed results

final_score: rating: 7: Good paper, accept
review_content: This paper presents a simple modification to the standard BiLSTM architecture for sentence classification that results in increased performance over a number of tasks. The basic idea is to encode sentence suffixes using multiple passes through an LSTM (rather than just a single pass as in the backwards side of a BiLSTM). While the gains are quite small over the InferSent baseline on most tasks, overall I think the results are good enough to make NLP researchers think about trying it out to squeeze out some extra accuracy (although the computational inefficiency might make them think twice! it would be good to mention the training time in the paper). The paper does not attempt to explain *why* these suffix representations are better than standard BiLSTM ones, although three pages is not much room; nevertheless, I wonder if looking at examples for which your model is correct but InferSent is wrong would be informative.

Additionally, it would be nice to see mean/variance across multiple runs reported in the results tables, as there is really no reason to only run one experiment on these small datasets, and the reported accuracy differences are often miniscule. 

Overall, I would lean towards accepting this paper, as I think its empirical contribution is appropriate for a workshop submission despite an analysis of why the method works. 
review_title: Solid focused contribution

final_score: rating: 6: Marginally above acceptance threshold
review_content: The paper proposes a sentence representation method by encoding the suffixes of word sequence in a sentence with BiLSTMs. In the forward direction, the proposed model encodes both prefixes and suffixes. To show the effectiveness, the SufiSent models were trained and tested on SNLI and a transfer SentEval benchmark and achieved better performance than the baseline, BiLSTM-Max (Conneau et al. 2017). The paper compares several variants of SufiSent such as tied parameters and concatenation of vectors.

The paper proposes an interesting extension (though novelty is rather limited) to the original BiLSTM encoders. Content of the paper is well structured and easy to follow. 

Pros: 
1. This is an interesting extension to the original BiLSTM encoders. 
2. Experiments show some improvement on the SNLI and the transfer tasks.

Cons:
1. Some more analysis and discussion on the benefit of encoding suffixes would help readers understand the emperical improvement.
2. The suffix encoding needs a total of N passes over progressively smaller suffixes of sentence. It may have a higher computation cost than the baseline. It would be interesting to provide some comparison on speed between baselines and the proposed models.


review_title: Interesting incremental work

Meta-review: Congratulations, your paper was accepted to the ICLR workshop.
Decision: Accept