Venue: ICLR 2017 conference

Reviews:

final_score: rating: null
review_content: My main question is the difference between the authors approach with K&G. The score for the arc (computed from parent child bilinear form) is used the same way in K&G?
review_title: questions

final_score: rating: null
review_content: Other than the computational benefits of the biaffine attention, why should I expect it to work better than the traditional attention mechanism. In other words, what is the intuition? Is the learned attention interpretable?
review_title: Intuition for biaffine attention

final_score: rating: null
review_content: The memory complexity of the proposed approach to label classification is O(dnc + n^2c) with c is the number of labels. The traditional approach requires O(dn^2 + cn). The authors claim that the proposed approach requires less memory if c is significantly smaller than the length of the longest sequence in the dataset. This is of course the worst case. But on average, the traditional approach requires much less memory because average(n) = 23 (Pen Treebank) and c is about 40 (universal dependencies). Am I right? 
review_title: memory complexity

final_score: rating: null
review_content: I think that what proposed in the paper is an extension of Kiperwasser & Goldberg (2016). The main (and only?) difference is how to score word pairs. So the point of "using general-purpose neural network components..." (abstract) is not innovative at all. In fact, the authors should cite to K&G paper for this point. 
review_title: related to Kiperwasser & Goldberg (2016)

final_score: rating: 6: Marginally above acceptance threshold
review_content: This is primarily an engineering paper. The authors find a small architectural modification to prior work and some hyperparameter tuning which pushes up the state-of-the-art in dependency parsing in two languages.

The architecture modification is a biaffine attention mechanism, which was inspired work in neural machine translation by Luong et al. (2015). The proposed attention model appears to be a win-win: better accuracy, reduced memory requirements, and fewer parameters.

The performance of the model is impressive, but how the performance is achieved is not very impressive. I do not believe that there are novel insights in the paper that will generalize to other tasks, nor does the paper shed light on the dependency parsing tasks (e.g., does biaffine attention have a linguistic interpretation?).

review_title: Final review

final_score: rating: 5: Marginally below acceptance threshold
review_content: The paper brings the new STOA in PTB dependency parsing. The numbers are very impressive.

Built upon the framework of K&G parser, this improvement is achieved by mainly two things -- (1) the paper replace the original scorer using bilinear scorer and make a difference between the head of modifier representation (2) the hyperparameter tuning in the ADAM trainer.

Although I think the bilinear modification make some sense intuitively, I don't think this contribution alone is strong enough for a conference publication. The authors did not show a good explanation of why this approach works better in this case nor did the author show this modification is generally applicable in any other tasks. 
review_title: official review

final_score: rating: 5: Marginally below acceptance threshold
review_content: The paper proposes a new function for computing arc score between two words in a sentence for dependency parsing. The proposed function is biaffine in the sense that it's a combination of a bilinear score function and a bias term playing a role as prior. The paper reports new state-of-the-art dependency parsing performances on both English PTB and Chinese TB.

The paper is very well written with impressive experimental results and analysis. However, the idea is hardly novel regarding to the theme of the conference: the framework that the paper uses is from Kiperwasser & Goldberg (2016), the use of bilinear score function for attention is from Luong et al (2015). Projecting BiLSTM outputs into different spaces using MLPs is a trivial step to make the model "deeper", whereas adding linear bias terms isn't confirmed to work in the experiments (table 2 shows that diag bilinear has a close performance to biaffine). 

I think that this paper is more proper for NLP conferences. 
review_title: final review

final_score: rating: null
review_content: Do you have any results without using dropout to confirm the second explanation "the POS tag dropout prevented the model from heavily relying on tags"? 
To my knowledge, gold POS tags are also very helpful for UAS. Was the POS tag dropout harmful for UAS as well? 
review_title: Experiments on CTB

Meta-review: The area chair disagrees with the reviewers and actually thinks this is a very important contribution. This paper has the potential to be have huge impact as it sets a new state of the art on a 20+ year old benchmark with a simple model. The simplicity of the model is what is so impressive, because it resets how people think about syntactic parsing as a task. The contributions in this paper have the potential to unleash a series of model simplifications in a number of areas and the area chair therefore strongly suggests accepting the paper.
 
 It is true that the techniques used in this paper are not new inventions but rather a careful combination of ideas proposed in other places. However, in the area chair's opinion this is a substantial contribution. There are lots of ideas out there and knowing which ideas to pick and how to combine them is very valuable. Showing that a simple model can beat more complicated models advances our understanding much more than a new technique that adds unnecessary additional complexity. The focus on novel models in academia is too big, leading to a proliferation of models that nobody needs.
Decision: Accept (Poster)