Neural Tree Transducers for Tree to Tree Learning
We introduce a novel approach to tree-to-tree learning, the neural tree transducer (NTT), a top-down depth first context-sensitive tree decoder, which is paired with recursive neural encoders. Our method works purely on tree-to-tree manipulations rather than sequence-to-tree or tree-to-sequence and is able to encode and decode multiple depth trees. We compare our method to sequence-to-sequence models applied to serializations of the trees and show that our method outperforms previous methods for tree-to-tree transduction.
 There may be some interesting ideas here, but I think in many places the mathematical description is very confusing and/or flawed. To give some examples: * Just before section 2.1.1, P(T) = .prod_{p .in Path(T)} ... : it*s not clear at all clear that this defines a valid distribution over trees. There is an implicit order over the paths in Path(T) that is simply not defined (otherwise how for x^p could we decide which symbols x^1 ... x^{p-1} to condition upon?) * "We can write S -> O | v | .epsilon..." with S, O and v defined as sets. This is certainly non-standard notation, more explanation is needed. * "The observation is generated by the sequence of left most production rules". This appears to be related to the idea of left-most derivations in context-free grammars. But no discussion is given, and the writing is again vague/imprecise. * "Although the above grammar is not, in general, context free" - I*m not sure what is being referred to here. Are the authors referring to the underlying grammar, or the lack of independence assumptions in the model? The grammar is clearly context-free; the lack of independence assumptions is a separate issue. * "In a probabilistic context-free grammar (PCFG), all production rules are independent": this is not an accurate statement, it*s not clear what is meant by production rules being independent. More accurate would be to say that the choice of rule is conditionally independent of all other information earlier in the derivation, once the non-terminal being expanded is conditioned upon.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Neural Tree Transducers for Tree to Tree Learning
We introduce a novel approach to tree-to-tree learning, the neural tree transducer (NTT), a top-down depth first context-sensitive tree decoder, which is paired with recursive neural encoders. Our method works purely on tree-to-tree manipulations rather than sequence-to-tree or tree-to-sequence and is able to encode and decode multiple depth trees. We compare our method to sequence-to-sequence models applied to serializations of the trees and show that our method outperforms previous methods for tree-to-tree transduction.
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>



 There may be some interesting ideas here, but I think in many places the mathematical description is very confusing and/or flawed. To give some examples: * Just before section 2.1.1, P(T) = .prod_{p .in Path(T)} ... : it*s not clear at all clear that this defines a valid distribution over trees. There is an implicit order over the paths in Path(T) that is simply not defined (otherwise how for x^p could we decide which symbols x^1 ... x^{p-1} to condition upon?) * "We can write S -> O | v | .epsilon..." with S, O and v defined as sets. This is certainly non-standard notation, more explanation is needed. * "The observation is generated by the sequence of left most production rules". This appears to be related to the idea of left-most derivations in context-free grammars. But no discussion is given, and the writing is again vague/imprecise. * "Although the above grammar is not, in general, context free" - I*m not sure what is being referred to here. Are the authors referring to the underlying grammar, or the lack of independence assumptions in the model? The grammar is clearly context-free; the lack of independence assumptions is a separate issue. * "In a probabilistic context-free grammar (PCFG), all production rules are independent": this is not an accurate statement, it*s not clear what is meant by production rules being independent. More accurate would be to say that the choice of rule is conditionally independent of all other information earlier in the derivation, once the non-terminal being expanded is conditioned upon.
assistant
 There may be some interesting ideas here, but I think in many places the mathematical description is very confusing and/or flawed. To give some examples: * Just before section 2.1.1, P(T) = .prod_{p .in Path(T)} ... : it*s not clear at all clear that this defines a valid distribution over trees. There is an implicit order over the paths in Path(T) that is simply not defined (otherwise how for x^p could we decide which symbols x^1 ... x^{p-1} to condition upon?) * "We can write S -> O | v | .epsilon..." with S, O and v defined as sets. This is certainly non-standard notation, more explanation is needed. * "The observation is generated by the sequence of left most production rules". This appears to be related to the idea of left-most derivations in context-free grammars. But no discussion is given, and the writing is again vague/imprecise. * "Although the above grammar is not, in general, context free" - I*m not sure what is being referred to here. Are the authors referring to the underlying grammar, or the lack of independence assumptions in the model? The grammar is clearly context-free; the lack of independence assumptions is a separate issue. * "In a probabilistic context-free grammar (PCFG), all production rules are independent": this is not an accurate statement, it*s not clear what is meant by production rules being independent. More accurate would be to say that the choice of rule is conditionally independent of all other information earlier in the derivation, once the non-terminal being expanded is conditioned upon.