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].
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Question: This paper introduces UCCA as a target representation for semantic parsing and also describes a quite successful transition-based parser for inference into that representation. I liked this paper a lot. I believe there is a lot of value simply in the introduction of UCCA (not new, but I believe relatively new to this community), which has the potential to spark new thinking about semantic representations of text. I also think the model was well thought out. While the model itself was fairly derivative of existing transition-based schemes, the extensions the authors introduced to make the model applicable in this domain were reasonable and well-explained, at what I believe to be an appropriate level of detail.  The empirical evaluation was pretty convincing -- the results were good, as compared to several credible baselines, and the authors demonstrated this performance in multiple domains. My biggest complaint about this paper is the lack of multilingual evaluation, especially given that the formalism being experimented with is exactly one that is supposed to be fairly universal. I'm reasonably sure multilingual UCCA corpora exist (in fact, I think the "20k leagues" corpus used in this paper is one such), so it would be good to see results in a language other than English.  One minor point: in section 6, the authors refer to their model as "grammarless", which strikes me as not quite correct. It's true that the UCCA representation isn't derived from linguistic notions of syntax, but it still defines a way to construct a compositional abstract symbolic representation of text, which to me, is precisely a grammar. (This is clearly a quibble, and I don't know why it irked me enough that I feel compelled to address it, but it did.)  Edited to add: Thanks to the authors for their response.

Answer: en


Question: Thank you for an interesting read.  I found this paper very interesting. Since I don't think (deterministic) approximate inference is separated from the modelling procedure (cf. exact inference), it is important to allow the users to select the inference method to suit their needs and constraints. I'm not an expert of PPL, but to my knowledge this is the first package that I've seen which put more focus on compositional inference. Leveraging tensorflow is also a plus, which allows flexible computation graph design as well as parallel computation using GPUs.  The only question I have is about the design of flexible objective functions to learn hyper-parameters (or in the paper those variables associated with delta q distributions). It seems hyper-parameter learning is also specified as inference, which makes sense if using MAP. However the authors also demonstrated other objective functions such as Renyi divergences, does that mean the user need to define a new class of inference method whenever they want to test an alternative loss function?

Answer: en


Question: Abstract: Needs to have a definition of ERP - can't assume the reader knows what this means.  Intro: Avoid 1 sentence paragraphs (page 1) The introduction is rather long - it seems to actually be two different sections: an introduction (1st four paragraphs) and 1+ page of background and hypothesis. Overall - at 1.5 pages the intro is rather long for a paper that is 4 pages total.  Methodology: I think there are a lot of assumptions in regards to what ERP are and how they work.  While the paper is a statistical study, it would have benefited with a context of an actual example.  The samples are from small to medium size companies (how many?) with 49 use case (how do these relate?).  Results: Discussion is too limited - it assumes that a reader is very familiar with the area, and that may not be the case.

Answer:
en