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: - Strengths: 1) an interesting task, 2) the paper is very clearly written, easy to follow, 3) the created data set may be useful for other researchers, 4) a detailed analysis of the performance of the model.  - Weaknesses: 1) no method adapted from related work for a result comparison 2) some explanations about the uniqueness of the task and discussion on limitations of previous research for solving this problem can be added to emphasize the research contributions further.   - General Discussion: The paper presents supervised and weakly supervised models for frame classification in tweets. Predicate rules are generated exploiting language-based and Twitter behavior-based signals, which are then supplied to the probabilistic soft logic framework to build classification models. 17 political frames are classified in tweets in a multi-label classification task. The experimental results demonstrate the benefit of the predicates created using the behavior-based signals. Please find my more specific comments below:  The paper should have a discussion on how frame classification differs from stance classification. Are they both under the same umbrella but with different levels of granularity?  The paper will benefit from adding a brief discussion on how exactly the transition from long congressional speech to short tweets adds to the challenges of the task. For example, does past research rely on any specific cross-sentential features that do not apply to tweets? Consider adapting the method of a frame classification work on congressional speech (or a stance classification work on any text) to the extent possible due to its limitations on Twitter data, to compare with the results of this work.  It seems “weakly supervised” and “unsupervised” – these two terms have been interchangeably used in the paper (if this is not the case, please clarify in author response). I believe "weakly supervised" is the more technically correct terminology under the setup of this work that should be used consistently throughout. The initial unlabeled data may not have been labeled by human annotators, but the classification does use weak or noisy labels of some sort, and the keywords do come from experts. The presented method does not use completely unsupervised data as traditional unsupervised methods such as clustering, topic models or word embeddings would.    The calculated Kappa may not be a straightforward reflection of the difficulty of frame classification for tweets (lines: 252-253), viewing it as a proof is a rather strong claim. The Kappa here merely represents the annotation difficulty/disagreement. Many factors can contribute to a low value  such as poorly written annotation guidelines, selection of a biased annotator, lack of annotator training etc. (on top of any difficulty of frame classification for tweets by human annotators, which the authors actually intend to relate to). 73.4% Cohen’s Kappa is strong enough for this task, in my opinion, to rely on the annotated labels.   Eq (1) (lines: 375-377) will ignore any contextual information (such as negation or conditional/hypothetical statements impacting the contributing word) when calculating similarity of a frame and a tweet. Will this have any effect on the frame prediction model? Did the authors consider using models that can determine similarity with larger text units such as perhaps using skip thought vectors or vector compositionality methods?    An ideal set up would exclude the annotated data from calculating statistics used to select the top N bi/tri-grams (line: 397 mentions entire tweets data set has been used), otherwise statistics from any test fold (or labeled data in the weakly supervised setup) still leaks into the selection process. I do not think this would have made any difference in the current selection of the bi/tri-grams or results as the size of the unlabeled data is much larger, but would have constituted a cleaner experimental setup.    Please add precision and recall results in Table 4.   Minor: please double check any rules for footnote placements concerning placement before or after the punctuation.

Answer: en


Question: Vanishing and exploding gradients makes the optimization of RNNs very challenging. The issue becomes worse on tasks with long term dependencies that requires longer RNNs. One of the suggested approaches to improve the optimization is to optimize in a way that the transfer matrix is almost orthogonal. This paper investigate the role of orthogonality on the optimization and learning which is very important. The writing is sound and clear and arguments are easy to follow. The suggested optimization method is very interesting. The main shortcoming of this paper is the experiments which I find very important and I hope authors can update the experiment section significantly. Below I mention some comments on the experiment section:  1- I think the experiments are not enough. At the very least, report the result on the adding problem and language modeling task on Penn Treebank.  2- I understand that the copying task becomes difficult with non-lineary. However, removing non-linearity makes the optimization very different and therefore, it is very hard to conclude anything from the results on the copying task.  3- I was not able to find the number of hidden units used for RNNs in different tasks.  4- Please report the running time of your method in the paper for different numbers of hidden units, compare it with the SGD and mention the NN package you have used.  5- The results on Table 1 and Table 2 might also suggest that the orthogonality is not really helpful since even without a margin, the numbers are very close compare to the case when you find the optimal margin. Am I right?  6- What do we learn from Figure 2? It is left without any discussion.

Answer: en


Question: Los autores hacen un análisis comparativo   entre diversas tecnologias para automación industrial. El análisis me pareció razonablemente profundo y amplio, pero lo califique como "weak accept" por no tener contribuciones científicas fuertes.  El texto necesita ser revisado, hay frases largas (por ejemplo la segunda del segundo parágrafo de la introducción tiene 11 líneas) y puntuaciones faltando o colocadas incorrectamente. La primera frase del segundo parágrafo de la introducción.  Incluir también el significado de las siglas PLC y PC.

Answer:
es


