From: NIPS.cc/2018/Workshop/IRASL
title: Acceptance Decision
decision: Accept

From: NIPS.cc/2018/Workshop/IRASL/Paper51/AnonReviewer2
title: Review for "Learning Distributed Representations of Symbolic Structure Using Binding and Unbinding Operations "
review: The paper proposes a Tensor Product Representations (TPRs) units that replaces LSTM or GRUs cells in a recurrent neural network. The idea is that these tensor product representations could potentially capture structured information needed for structured tasks such as natural language processing. The authors evaluated the models (and showed improvements) on language entailment and then other downstream NLP tasks such as sentiment analysis, etc.


Overall, an interesting approach to learning structured data in language, especially when there isnt extra overhead in terms of parameters and training. The experiments showed positive results, the authors run ablations studies on how the number of role vectors effect performance. 

I am curious if this can be used for tasks such as language modeling and if the model could indeed capture more structured information in language and help to generate more structured languages. I would be curious to see some samples from the model trained on language modeling tasks.

I am also curious to see if this model is more or less prune to adversarial examples, especially in the setting of question answering.

rating: 4: Top 50% of accepted papers, clear accept
confidence: 2: The reviewer is fairly confident that the evaluation is correct

From: NIPS.cc/2018/Workshop/IRASL/Paper51/Authors
title: Thanks for your review and comments!
comment: We really appreciate your time and effort in reviewing our paper. Our work was inspired by TPRs and recent work on incorporating TPRs into neural network designs, and the proposed TPRU has significantly fewer parameters than widely-used LSTM and GRU. It indeed provides better performance on Multi-genre natural language task and similar performance on Logical Entailment task.

We are sorry that Table 1 is too dense, and it is because of the page limit. We will revise Table 1 in our next version. 

In logical entailment task, the test set contains five subsets at different difficulty levels, including “easy”,”hard”,”big”,”massive”, and ”exam”. The naming for each subset is given by the difficulty level which is determined by the average number of variables in logical propositions. The number under the name of a subset in Table 1 represents the number of rows required in the truth table in order to solve the task directly without learning. For example, on average, a pair of propositions in the "easy" set requires 81 rows in a truth table to solve it, and the "massive" set requires 848,570.0 rows.

From: NIPS.cc/2018/Workshop/IRASL/Paper51/AnonReviewer1
title: Learning Distributed Representations of Symbolic Structure Using Binding and Unbinding Operations
review: The paper proposed a novel recurrent neural network based on Tensor Product Representations (TPRs). The idea is to encourage the learned distributed representations to encode more structure-related information, that are hopefully able to better disentangle the underlying structure of the data. The paper extends previous works on TPR by adding multiple bindings TPRs into RNN representations. The experiments, reported on some NLP tasks, have confirmed the effectiveness of this approach, that often gives better performance with a reduced set of parameters.

According to me, the idea totally makes sense, especially in  NLP, where using this representation based on a composition of sub-structures could induce a very powerful inductive bias that can help to solve challenging tasks efficiently. The paper is also rather clear and well written. 

Minor comment:
Table 2 is too dense and not fully clear to me. What is for instance “easy”,”hard”,”big”,”massive”,”exam”?

rating: 4: Top 50% of accepted papers, clear accept
confidence: 2: The reviewer is fairly confident that the evaluation is correct