Venue: ICLR 2017 conference

Reviews:

final_score: rating: null
review_content: — Explain the differences between a stack LSTM and Example 6 (compositional representation from arc-standard dependency parsing). It’s clear to me that they are indeed different, but I’d love to better understand the nuances there, and it seems like an important comparison to draw explicitly in the paper.
— Any plans to release accompanying code?
— How does the concurrent ICLR submission https://arxiv.org/pdf/1611.01734v2.pdf fit into this framework, if at all?

review_title: Questions

final_score: rating: 5: Marginally below acceptance threshold
review_content: Overall, this is a nice paper. Developing a unifying framework for these newer
neural models is a worthwhile endeavor.

However, it's unclear if the DRAGNN framework (in its current form) is a
significant standalone contribution. The main idea is straightforward: use a
transition system to unroll a computation graph. When you implement models in
this way you can reuse code because modules can be mixed and matched. This is
nice, but (in my opinion) is just good software engineering, not machine 
learning research.

Moreover, there appears to be little incentive to use DRAGNN, as there are no
'free things' (benefits) that you get by using the framework. For example:

- If you write your neuralnet in an automatic differentiation library (e.g.,
  tensorflow or dynet) you get gradients for 'free'.

- In the VW framework, there are efficiency tricks that 'the credit assignment
  compiler' provides for you, which would be tedious to implement on your
  own. There is also a variety of algorithms for training the model in a
  principled way (i.e., without exposure bias).

I don't feel that my question about the limitations of the framework has been
satisfactorily addressed. Let me ask it in a different way: Can you give me
examples of a few models that I can't (nicely) express in the DRAGNN framework?
What if I wanted to implement https://openreview.net/pdf?id=HkE0Nvqlg or
http://www.cs.jhu.edu/~jason/papers/rastogi+al.naacl16.pdf? Can I implement the
dynamic programming components as transition units and (importantly) would it be
efficient?

 disagree that the VW framework is orthogonal, it is a *competing* way to
implement recurrent models. The main different to me appears to be that VW's
imperative framework is more general, but less modular.

The experimental contribution seems useful as does the emphasis on how easy it
is to incorporate multi-task learning.

Minor:

- It would be useful to see actual code snippets (possibly in an
  appendix). Otherwise, its unclear how modular DRAGNN really are.

- The introduction states that (unlike seq2seq+attention) inference remains
  linear. Is this *necessarily* the case? Users define a transition system that
  is quadratic, just let attention be over all previous states. I recommend that
  authors rephrase statement more carefully.

- It seems strange to use A() as in "actions", then use d as "decision" for its
  elements.

- I recommend adding i as an argument to the definition of the recurrence
  function r(s) to make it clear that it's the subset of previous states at time
  i, otherwise it looks like an undefined variable. A nice terse option is to
  write r(s_i).

- Real numbers should be \mathbb{R} not \mathcal{R}.

- It's more conventional to use t for a time-step instead of i.

- Example 2: "52 feature embeddings" -> did you mean "52-DIMENSIONAL feature
  embeddings"?

review_title: Final review

final_score: rating: null
review_content: 1. What are the limitations of the DRAGNN framework? (e.g., Can we implement seq2seq with soft attention?)

2. How does DRAGNN related to Vowpal Wabbit's imperative learning-to-search learning framework?
See recent paper at NIPS https://arxiv.org/abs/1406.1837 (which has been on arxiv since 2014) as well as the author's tutorial at ICML 2015.

3. Will a framework be released accompanying paper? The examples given are a little hand-wavey, which got me wondering: How many lines of code are required to expresses each of the models? Also, How long does it take to train and run?

4. How does one include beam search or dynamic programming inference with a DRAGNN? To implement the full Parsey McParseFace model, users need this to implement approx global normalization and decoding via beam search.

5. Why limit training to maximum likelihood training, especially given that we know such training is prone to failure due to "exposure bias" and many good alternatives exist, including DAgger (Ross et. al, 2011), LOLS (Chang et al., 2015) and reinforcement learning algorithms (e.g., policy gradient).
review_title: Limitation of the framework

final_score: rating: 7: Good paper, accept
review_content: The authors present a general framework for defining a wide variety of recurrent neural network architectures, including seq2seq models, tree-structured models, attention, and a new family of dynamically connected architectures. The framework defines a new, general-purpose recurrent unit called the TBRU, which takes a transition system, defining and constraining its inputs and outputs, and input function which defines the mapping between raw inputs and fixed-width vector representations, and recurrence function that defines the inputs to each recurrent step as a function of the current state, and an RNN cell that computes the output from the input (fixed and recurrent). Many example instantiations of this framework are provided, including sequential tagging RNNs, Google’s Parsey McParseface parser, encoder/decoder networks, tree LSTMs and less familiar examples that demonstrate the power this framework. 

The most interesting contribution of this work is the ease by which it can be used to incorporate dynamic recurrent connections through the definition of the transition system. In particular, this paper explores the application of these dynamic connections to syntactic dependency parsing, both as a standalone task, and by multitasking parsing with extractive summarization, using the same compositional phrase representations as features for the parser and summarization (previous work used discrete parse features), which is particularly simple/elegant in this framework. In experimental results, the authors demonstrate that such multitasking leads to more accurate summarization models, and using the framework to incorporate more structure into existing parsing models also leads to increased accuracy with no big-oh efficiency loss (compared with e.g. attention). 

The “raison d’etre,” in particular the example, perhaps described even more thoroughly/explicitly, should be made as clear as possible as soon as possible. This is the most important contribution, but it gets lost in the description and presentation as a framework — emphasizing that attention, seq2seq, etc can be represented in the framework is distracting and makes it seem less novel than it is. AnonReviewer6 clearly missed this point, as did I in my first pass over the paper. To get this idea across and to emphasize the benefits of this representation, I’d love to see more detailed analysis of these representations and their importance to achieving your experimental results. I think it would also be helpful to emphasize the difference between a stack LSTM and Example 6. 

Overall I think this paper presents a valuable contribution, though the exposition could be improved and analysis of experimental results expanded. 
review_title: null

final_score: rating: 6: Marginally above acceptance threshold
review_content: The paper proposes a new neural architecture, called DRAGNN, for the transition-based framework. A DRAGNN uses TBRUs which are neural units to compute hidden activations for the current state of a transition-based system. The paper proves that DRAGNNs can cover a wide range of transition-based methods in the literature. In addition, one can easily implement multitask learning systems with DRAGNNs. The experimental results shows that using DRAGNNs the authors built (near) state-of-the-art systems for 2 tasks: parsing and summarization. 

The paper contains two major parts: DRAGNN and demonstrations of its usages. 

Regarding to the first part, the proposed DRAGNN is a neat tool for building any transition-based systems. However, it is difficult to say whether the DRAGNN is novel. Transition-based framework is already well defined and there's a huge trend in NLP using neural networks to implement transition-based systems. In my opinion, the difference between the Stack-LSTM (Dyer et al., 2015) and DRAGNN is slight. Of course, the DRAGNN is a powerful architecture but the contribution here should be considered mainly in terms of software engineering.

In the second part, the authors used DRAGNN to implement new transition-based systems for different (multi-)tasks. The implementations are neat, confirming that DRAGNN is a powerful architecture, especially for multitask learning. However, we should bear in mind that the solutions employed are already there in the literature, thus making difficult to judge the novelty of this part w.r.t. the theme of the conference.  
review_title: official review

Meta-review: This work proved to be a controversial submission. This paper has two main components: 1) a neural framework TBRU/DRAGNN, 2) experimental results on some NLP tasks. Generally there was lack of consensus about the originality of (1) and a general feeling that even if there are aspect of novelties, that the paper was lacking clarity about is contributions One reviewer was an outlier, highlighting the benefit of the ability "incorporate dynamic recurrent connections through the definition of the transition system" which is claimed to be really novel. Others claim this is specific to the framework used. Negative reviewers felt that (1) is probably not novel within itself and represents a slight departure from stack-lstm. The controversy here is whether DRAGNN is simple "software engineering with no inherent "free things" that would lead to impact within the community. This question of impact is also inherent to (2), in particular whether the authors really got new benefit of using DragNN or whether these are "reimplementations of things in the literature". I felt the reviewers did seem to put in due diligence here, so the recommendation would be to put further effort into clarification and further back up of novelty claims in future versions.
Decision: Reject