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].

[Q]: Taking into account the loss in the binarization step through a proximal Newton algorithm is a nice idea. This is at least one approach to bringing in the missing loss in the binarization step, which has recently gone from a two step process of train and binarize to a single step simultaneous train/compress. Performance on a few small tasks show the benefit. It would be nice to see some results on substantial networks and tasks which really need compression on embedded systems (a point made in the introduction). Is it necessary to discuss exploding/vanishing gradients when the RNN experiments are carried out by an LSTM, and handled by the cell error carousel? We see the desire to tie into proposition 2, but not clear that the degradation we see in the binary connect is related. Adam is used in the LSTM optimization, was gradient clipping really needed, or is the degradation of binary connect simply related to capacity? For proposition 3.1, theorem 3.1 and proposition 3.2 put the pointers to proofs in appendix.
[A]: en

[Q]: This paper describes a system to assist written test scoring. - Strengths: The paper represents an application of an interesting NLP problem -- recognizing textual entailment -- to an important task -- written test scoring. - Weaknesses: There isn't anything novel in the paper. It consist of an application of an existing technology to a known problem. The approach described in the paper is not autonomous -- it still needs a human to do the actual scoring. The paper lacks any quantitative or qualitative evaluation of how useful such system is. That is, is it making the job of the scorer easier? Is the scorer more effective as compared to not having automatic score? The system contains multiple components and it is unclear how the quality of each one of them contributes to the overall experience. The paper needs more work with the writing. Language and style is rough in several places. The paper also contains several detailed examples, which don't necessarily add a lot of value to the discussion. For the evaluation of classification, what is the baseline of predicting the most frequent class? - General Discussion: I find this paper not very inspiring. I don't see the message in the paper apart from announcing having build such a system
[A]: en

[Q]: This paper compares different ways of inducing embeddings for the task of polarity classification. The authors focus on different types of corpora and find that not necessarily the largest corpus provides the most appropriate embeddings for their particular task but it is more effective to consider a corpus (or subcorpus) in which a higher concentration of subjective content can be found. The latter type of data are also referred to as "task-specific data". Moreover, the authors compare different embeddings that combine information from "task-specific" corpora and generic corpora. A combination outperforms embeddings just drawn from a single corpus. This combination is not only evaluated on English but also on a less resourced language (i.e. Catalan). - Strengths: The paper addresses an important aspect of sentiment analysis, namely how to appropriately induce embeddings for training supervised classifers for polarity classification. The paper is well-structured and well-written. The major claims made by the authors are sufficiently supported by their experiments. - Weaknesses: The outcome of the experiments is very predictable. The methods that are employed are very simple and ad-hoc. I found hardly any new idea in that paper. Neither are there any significant lessons that the reader learns about embeddings or sentiment analysis. The main idea (i.e. focusing on more task-specific data for training more accurate embeddings) was already published in the context of named-entity recognition by Joshi et al. (2015). The additions made in this paper are very incremental in nature. I find some of the experiments inconclusive as (apparently) no statistical signficance testing between different classifiers has been carried out. In Tables 2, 3 and 6, various classifier configurations produce very similar scores. In such cases, only statistical signficance testing can really give a proper indication whether these difference are meaningful. For instance, in Table 3 on the left half reporting results on RT, one may wonder whether there is a significant difference between "Wikipedia Baseline" and any of the combinations. Furthermore, one doubts whether there is any signficant difference between the different combinations (i.e. either using "subj-Wiki", "subj-Multiun" or "subj-Europarl") in that table. The improvement by focusing on subjective subsets is plausible in general. However, I wonder whether in real life, in particular, a situation in which resources are sparse this is very helpful. Doing a pre-selection with OpinionFinder is some pre-processing step which will not be possible in most languages other than English. There are no equivalent tools or fine-grained datasets on which such functionality could be learnt. The fact that in the experiments for Catalan, this information is not considered proves that. Minor details: - lines 329-334: The discussion of this dataset is confusing. I thought the task is plain polarity classification but the authors here also refer to "opinion holder" and "opinion targets". If these information are not relevant to the experiments carried out in this paper, then they should not be mentioned here. - lines 431-437: The variation of "splicing" that the authors explain is not very well motivated. First, why do we need this? In how far should this be more effective than simple "appending"? - lines 521-522: How is the subjective information isolated for these configurations? I assume the authors here again employ OpinionFinder? However, there is no explicit mention of this here. - lines 580-588: The definitions of variables do not properly match the formula (i.e. Equation 3). I do not find n_k in Equation 3. - lines 689-695: Similar to lines 329-334 it is unclear what precise task is carried out. Do the authors take opinion holders and targets in consideration? ***AFTER AUTHORS' RESPONSE*** Thank you very much for these clarifying remarks. I do not follow your explanations regarding the incorporation of opinion holders and targets, though. Overall, I will not change my scores since I think that this work lacks sufficient novelty (the things the authors raised in their response are just insufficient to me). This submission is too incremental in nature.
[A]:
en