Venue: ICLR 2018 Workshop

Reviews:

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: The paper proposes a method to encode and decode symbolic representations in vectors using neural networks. This is a very important problem in neural networks. Yet, the paper forgets to mention that this topic has been revitalized some years ago within the distributed tree kernels and within the compositional distributional semantics model (Zanzotto, Ferroni, Baroni, 2015). 
review_title: Back to symbols

final_score: rating: 6: Marginally above acceptance threshold
review_content: I am not sure  I understand this paper. 
review_title: not sure I understand this paper

final_score: rating: 7: Good paper, accept
review_content: Summary:

This paper studies the power of generic sequence to sequence models in representing general symbol structures. To do that, authors propose a simple formal language and generate structures and queries in this formal language. Then they train a sequence to sequence model with bidirectional LSTM encoder and an LSTM decoder. This simple model achieves high accuracy and also demonstrates superposition principle.


My Comments:

This is an interesting study which is worth reporting in the workshop track.

1. What is the effect of length of the expression? Does the performance degrade after certain length?
2. In Query-denoting expressions paragraph, correct “Afn S-LANG ..”

review_title: Good paper. Accept.

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