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:  [+] Well motivated, tackles an interesting problem;  [+] Clearly written and structured, accompanied by documented code and dataset;  [+] Encouraging results.  - Weaknesses:  [-] Limited to completely deterministic, hand-engineered minimization rules;  [-] Some relevant literature on OIE neglected;  [-] Sound but not thorough experimental evaluation.  - General Discussion:  This paper tackles a practical issue of most OIE systems, i.e. redundant, uninformative and inaccurate extractions. The proposed approach, dubbed MinOIE, is designed to actually "minimize" extractions by removing overly specific portions and turning them into structured annotations of various types (similarly to OLLIE). The authors put MinIE on top of a state-of-the-art OIE system (ClausIE) and test it on two publicly available datasets, showing that it effectively leads to more concise extractions compared to standard OIE approaches, while at the same time retaining accuracy.  Overall, this work focuses on an interesting (and perhaps underinvestigated) aspect of OIE in a sound and principled way. The paper is clearly written, sufficiently detailed, and accompanied by supplementary material and a neat Java implementation. My main concern is, however, with the entirely static, deterministic and rule-based structure of MinIE. Even though I understand that a handful of manually engineered rules is technically the best strategy when precision is key, these approaches are typically very hard to scale, e.g. in terms of languages (a recent trend of OIE, see Faruqui and Kumar, 2015; Falke et al., 2016). In other words, I think that this contribution somehow falls short of novelty and substance in proposing a pipeline of engineered rules that are mostly inspired by other OIE systems (such as ClausIE or ReVerb); for instance, I would have really appreciated an attempt to learn these minimization rules instead of hard-coding them.  Furthermore, the authors completely ignore a recent research thread on “semantically-informed” OIE (Nakashole et al., 2012; Moro and Navigli, 2012; 2013; Delli Bovi et al., 2015) where traditional extractions are augmented with links to underlying knowledge bases and sense inventories (Wikipedia, Wikidata, Yago, BabelNet). These contributions are not only relevant in terms of related literature: in fact, having text fragments (or constituents) explicitly linked to a knowledge base would reduce the need for ad-hoc minimization rules such as those in Sections 6.1 and 6.2. In the example with "Bill of Rights" provided by the authors (line 554), an OIE pipeline with a proper Entity Linking module would recognize automatically the phrase as mention of a registered entity, regardless of the shape of its subconstituents. Also, an underlying sense inventory would seamlessly incorporate the external information about collocations and multi-word expressions used in Section 6.2: not by chance, the authors rely on WordNet and Wiktionary to compile their dictionary of collocations.  Finally, some remarks on the experimental evaluation:  - Despite the claim of generality of MinIE, the authors choose to experiment only with ClausIE as underlying OIE system (most likely the optimal match). It would have been very interesting to see if the improvement brought by MinIE is consistent also with other OIE systems, in order to actually assess its flexibility as a post-processing tool.  - Among the test datasets used in Section 7, I would have included the recent OIE benchmark of Stanovsky and Dagan (2016), where results are reported also for comparison systems not included in this paper (TextRunner, WOIE, KrakeN).  References:  - Manaal Faruqui and Shankar Kumar. Multilingual Open Relation Extraction using Cross-lingual Projection. NAACL-HLT, 2015.  - Tobias Falke, Gabriel Stanovsky, Iryna Gurevych and Ido Dagan. Porting an Open Information Extraction System from English to German. EMNLP 2016.  - Ndapandula Nakashole, Gerhard Weikum and Fabian Suchanek. PATTY: A Taxonomy of Relational Patterns with Semantic Types. EMNLP 2012.  - Andrea Moro, Roberto Navigli. WiSeNet: Building a Wikipedia-based Semantic Network with Ontologized Relations. CIKM 2012.  - Andrea Moro, Roberto Navigli. Integrating Syntactic and Semantic Analysis into the Open Information Extraction Paradigm. IJCAI 2013.  - Claudio Delli Bovi, Luca Telesca and Roberto Navigli. Large-Scale Information Extraction from Textual Definitions through Deep Syntactic and Semantic Analysis. TACL vol. 3, 2015.  - Gabriel Stanovsky and Ido Dagan. Creating a Large Benchmark for Open Information Extraction. EMNLP 2016.

SOLUTION: en

PROBLEM: Summary: In this paper, the authors study ResNets through a theoretical formulation of a spin glass model. The conclusions are that ResNets behave as an ensemble of shallow networks at the start of training (by examining the magnitude of the weights for paths of a specific length) but this changes through training, through which the scaling parameter C (from assumption A4) increases, causing it to behave as an ensemble of deeper and deeper networks.  Clarity: This paper was somewhat difficult to follow, being heavy in notation, with perhaps some notation overloading. A summary of some of the proofs in the main text might have been helpful.  Specific Comments: - In the proof of Lemma 2, I'm not sure where the sequence beta comes from (I don't see how it follows from 11?)  - The ResNet structure used in the paper is somewhat different from normal with multiple layers being skipped? (Can the same analysis be used if only one layer is skipped? It seems like the skipping mostly affects the number of paths there are of a certain length?)  - The new experiments supporting the scale increase in practice are interesting! I'm not sure about Theorems 3, 4 necessarily proving this link theoretically however, particularly given the simplifying assumption at the start of Section 4.2?

SOLUTION: en

PROBLEM: Un muy buen trabajo, presenta integración de varias tecnologías de una forma clara y coherente  A pesar de que lo mencionan como trabajo futuro, se debería tener en cuenta el HL7 desde ya

SOLUTION:
es