instruction:
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].
question:
Tema bien tratado tecnicamente y en forma clara. El tema solo muestra un manejo tecnico sobre una herramienta, y no aporta algo novedoso. Se recomienda hacer una comparacion en complejidad sobe las SQL con datos primitivos versus a datos XML, y abarcar otros aspectos como por ejemplo la mejora, etc.
answer:
es

question:
This paper presents a set of experiments investigating what kinds of information are captured in common unsupervised approaches to sentence representation learning. The results are non-trivial and somewhat surprising. For example, they show that it is possible to reconstruct word order from bag of words representations, and they show that LSTM sentence autoencoders encode interpretable features even for randomly permuted nonsense sentences. Effective unsupervised sentence representation learning is an important and largely unsolved problem in NLP, and this kind of work seems like it should be straightforwardly helpful towards that end. In addition, the experimental paradigm presented here is likely more broadly applicable to a range of representation learning systems. Some of the results seem somewhat strange, but I see no major technical concerns, and think that that they are informative. I recommend acceptance. One minor red flag: - The massive drop in CBOW performance in Figures 1b and 4b are not explained, and seem implausible enough to warrant serious further investigation. Can you be absolutely certain that those results would appear with a different codebase and different random seed implementing the same model? Fortunately, this point is largely orthogonal to the major results of the paper. Two writing comments: - I agree that the results with word order and CBOW are surprising, but I think it's slightly misleading to say that CBOW is predictive of word order. It doesn't represent word order at all, but it's possible to probabilistically reconstruct word order from the information that it does encode. - Saying that "LSTM auto-encoders are more effective at encoding word order than word content" doesn't really make sense. These two quantities aren't comparable.
answer:
en

question:
- Strengths: Nicely written and understandable. Clearly organized. Targeted answering of research questions, based on different experiments. - Weaknesses: Minimal novelty. The "first sentence" heuristic has been in the summarization literature for many years. This work essentially applies this heuristic (evolved) in the keyword extraction setting. This is NOT to say that the work is trivial: it is just not really novel. Lack of state-of-the-art/very recent methods. The experiment on the system evaluation vs state-of-the-art systems simply uses strong baselines. Even though the experiment answers the question "does it perform better than baselines?", I am not confident it illustrates that the system performs better than the current state-of-the-art. This somewhat reduces the value of the paper. - General Discussion: Overall the paper is good and I propose that it be published and presented. On the other hand, I would propose that the authors position themselves (and the system performance) with respect to: MartinezRomo, Juan, Lourdes Araujo, and Andres Duque Fernandez. "SemGraph: Extracting keyphrases following a novel semantic graphbased approach." Journal of the Association for Information Science and Technology 67.1 (2016): 71-82. (with which the work holds remarkable resemblance in some points) Le, Tho Thi Ngoc, Minh Le Nguyen, and Akira Shimazu. "Unsupervised Keyphrase Extraction: Introducing New Kinds of Words to Keyphrases." Australasian Joint Conference on Artificial Intelligence. Springer International Publishing, 2016.
answer:
en