Tree-structured decoding with doubly-recurrent neural networks
We propose a neural network architecture for generating tree-structured objects from encoded representations. The core of the method is a doubly-recurrent neural network that models separately the width and depth recurrences across the tree, and combines them inside each cell to generate an output. The topology of the tree is explicitly modeled, allowing the network to predict both content and topology of the tree when decoding. That is, given only an encoded vector representation, the network is able to simultaneously generate a tree from it and predict labels for the nodes. We test this architecture in an encoder-decoder framework, where we train a network to encode a sentence as a vector, and then generate a tree structure from it. The experimental results show the effectiveness of this architecture at recovering latent tree structure in sequences and at mapping sentences to simple functional programs.
This paper proposes a variant of a recurrent neural network that has two orthogonal temporal dimensions that can be used as a decoder to generate tree structures (including the topology) in an encoder-decoder setting. The architecture is well motivated and I can see several applications (in addition to what*s presented in the paper) that need to generate tree structures given an unstructured data. One weakness of the paper is the limitation of experiments. IFTTT dataset seems to be an interesting appropriate application, and there is also a synthetic dataset, however it would be more interesting to see more natural language applications with syntactic tree structures. Still, I consider the experiments sufficient as a first step to showcase a novel architecture. A strength is that the authors experiment with different design decisions when building the topology predictor components of the architecture, about when / how to decide to terminate, as opposed to making a single arbitrary choice. I see future applications of this architecture and it seems to have interesting directions for future work so I suggest its acceptance as a conference contribution.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Tree-structured decoding with doubly-recurrent neural networks
We propose a neural network architecture for generating tree-structured objects from encoded representations. The core of the method is a doubly-recurrent neural network that models separately the width and depth recurrences across the tree, and combines them inside each cell to generate an output. The topology of the tree is explicitly modeled, allowing the network to predict both content and topology of the tree when decoding. That is, given only an encoded vector representation, the network is able to simultaneously generate a tree from it and predict labels for the nodes. We test this architecture in an encoder-decoder framework, where we train a network to encode a sentence as a vector, and then generate a tree structure from it. The experimental results show the effectiveness of this architecture at recovering latent tree structure in sequences and at mapping sentences to simple functional programs.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper proposes a variant of a recurrent neural network that has two orthogonal temporal dimensions that can be used as a decoder to generate tree structures (including the topology) in an encoder-decoder setting. The architecture is well motivated and I can see several applications (in addition to what*s presented in the paper) that need to generate tree structures given an unstructured data. One weakness of the paper is the limitation of experiments. IFTTT dataset seems to be an interesting appropriate application, and there is also a synthetic dataset, however it would be more interesting to see more natural language applications with syntactic tree structures. Still, I consider the experiments sufficient as a first step to showcase a novel architecture. A strength is that the authors experiment with different design decisions when building the topology predictor components of the architecture, about when / how to decide to terminate, as opposed to making a single arbitrary choice. I see future applications of this architecture and it seems to have interesting directions for future work so I suggest its acceptance as a conference contribution.
assistant
This paper proposes a variant of a recurrent neural network that has two orthogonal temporal dimensions that can be used as a decoder to generate tree structures (including the topology) in an encoder-decoder setting. The architecture is well motivated and I can see several applications (in addition to what*s presented in the paper) that need to generate tree structures given an unstructured data. One weakness of the paper is the limitation of experiments. IFTTT dataset seems to be an interesting appropriate application, and there is also a synthetic dataset, however it would be more interesting to see more natural language applications with syntactic tree structures. Still, I consider the experiments sufficient as a first step to showcase a novel architecture. A strength is that the authors experiment with different design decisions when building the topology predictor components of the architecture, about when / how to decide to terminate, as opposed to making a single arbitrary choice. I see future applications of this architecture and it seems to have interesting directions for future work so I suggest its acceptance as a conference contribution.