TASK DEFINITION: In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].
PROBLEM: - Strengths: The paper proposes an end-to-end neural model for semantic graph parsing, based on a well-designed transition system.  The work is interesting, learning semantic representations of DMRS, which is capable of resolving semantics such as scope underspecification. This work shows a new scheme for computational semantics, benefiting from an end-to-end transition-based incremental framework, which resolves the parsing with low cost.  - Weaknesses:   My major concern is that the paper only gives a very common introduction for the definition of DMRS and EP, and the example even makes me a little confused because I cannot see anything special for DMRS. The description can be a little more detailed, I think. However, upon the space limitation, it is understandable. The same problem exists for the transition system of the parsing model. If I do not have any background of MRS and EP, I can hardly learn something from the paper, just seeing that this paper is very good.  - General Discussion:   Overall, this paper is very interesting to me. I like the DMRS for semantic parsing very much and like the paper very much. Hope that the open-source codes and datasets can make this line of research being a hot topic.

SOLUTION: en

PROBLEM: This is an interesting paper about quantized networks that work on temporal difference inputs.  The basic idea is that when a network has only to process differences then this is computational much more efficient specifically with natural video data since large parts of an image would be fairly constant so that the network only has to process the informative sections of the image (video stream). This is of course how the human visual system works, and it is hence of interest even beyond the core machine learning community.   As an aside, there is a strong community interested in event-based vision such as the group of Tobi Delbrück, and it might be interesting to connect to this community. This might even provide a reference for your comments on page 1.  I guess the biggest novel contribution is that a rounding network can be replaced by a sigma-delta network, but that the order of discretization and summation doe make some difference in the actual processing load. I think I followed the steps and  Most of my questions have already been answers in the pre-review period. My only question remaining is on page 3, “It should be noted that when we refer to “temporal differences”, we refer not to the change in the signal over time, but in the change between two inputs presented sequentially. The output of our network only depends on the value and order of inputs, not on the temporal spacing between them.”  This does not make sense to me. As I understand you just take the difference between two frames regardless if you call this temporal or not it is a change in one frame. So this statement rather confuses me and maybe should be dropped unless I do miss something here, in which case some more explanation would be necessary.  Figure 1 should be made bigger.  An improvement of the paper that I could think about is a better discussion of the relevance of the findings. Yes, you do show that your sigma-delta network save some operation compared to threshold, but is this difference essential for a specific task, or does your solution has relevance for neuroscience?

SOLUTION: en

PROBLEM: This paper proposes a method for evaluating topic quality based on using word embeddings to calculate similarity (either directly or indirectly via matrix factorisation), achieving impressive results over standard datasets.  The proposed method represents a natural but important next step in the evolutionary path of research on topic evaluation. The thing that troubled me most with the results was that, while you achieve state-of-the-art results for all three datasets, there are large inconsistencies in which methods perform and which methods perform less well (below the state of the art). In practice, none of the proposed methods consistently beats the state of the art, and the SVD-based methods perform notably badly over the genomics dataset. For someone who wants to take your method off the shelf and use it over any arbitrary dataset, this is a considerable worry. I suspect that the lower results for SVD over genomics relate to the proportion of OOV terms (see comment below), and that it may be possible to automatically predict which method will perform best based on vocab match with GloVe etc., but there is no such discussion in the paper.  Other issues:  - the proposed method has strong similarities with methods proposed in the   lexical chaining literature, which I would encourage the authors to read up   on and include in any future version of the paper  - you emphasis that your method has no parameters, but the word embedding   methods have a large number of parameters, which are implicit in your   method. Not a huge deal, but worth acknowledging  - how does your method deal with OOV terms, e.g. in the genomics dataset   (i.e. terms not present in the pretrained GloVe embeddings)? Are they simply   ignored? What impact does this have on the method?  Low-level issues:  - in your description of word embeddings in Section 2.1, you implicitly assume   that the length of the vector is unimportant (in saying that cosine   similarity can be used to measure the similarity between two vectors). If   the vectors are unit length, this is unproblematic, but word2vec actually   doesn't return unit-length vectors (the pre-trained vectors have been   normalised post hoc, and if you run word2vec yourself, the vector length is   certainly not uniform). A small detail, but important.  - the graphs in Figure 1 are too small to be readable

SOLUTION:
en