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: Strengths  - interesting to explore the connection between ReLU DNN and simplified SFNN - small task (MNIST)  is used to demonstrate the usefulness of the proposed training methods experimentally - the proposed, multi-stage training methods are simple to implement (despite lacking theoretical rigor)   Weaknesses  -no results are reported on real tasks with large training set  -not clear exploration on the scalability of the learning methods when training data becomes larger  -when the hidden layers become stochastic, the model shares uncertainty representation with deep Bayes networks or deep generative models (Deep Discriminative and Generative Models for Pattern Recognition , book chapter in “Pattern Recognition and Computer Vision”, November 2015, Download PDF). Such connections should be discussed, especially wrt the use of uncertainty representation to benefit pattern recognition (i.e. supervised learning via Bayes rule) and to benefit the use of domain knowledge such as “explaining away”.  -would like to see connections with variational autoencoder models and training, which is also stochastic with hidden layers

A: en
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Q: - 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.

A: en
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Q: RESUMEN. El trabajo propone un modelo para evaluar la calidad de un software orientado a agentes. Básicamente, el modelo considera tres propiedades para evaluar su calidad: habilidad social, autonomía y pro-actividad.  Para evaluar estas propiedades, el autor descompone estas propiedades en varias sub-propiedades medibles. El autor evalúa este modelo a través de un caso estudio: un sistema bancario.  Comentarios Generales: De manera notaría, la estructura del documento tiene los aspectos necesarios para ser considerado un artículo científico. La contribución del artículo pareciese ser relevante.  Dicho esto, este trabajo carece de algunos puntos que (para mí) es importante abordar:  * El autor menciona que no hay modelo para evaluar la calidad del software de agente, y el propone uno. Su propuesta es una adaptación de ISO/IEC, sin embargo, el autor no justifica por qué es la apropiada.  * Con respecto al punto anterior, el párrafo 1 de la introducción dice que no hay modelo de que mida la calidad del software. Luego, el párrafo 2 menciona que hay algunos trabajos acerca de esto. El autor debería clarificar su contribución.  * El autor menciona que se desarrolló un software para este trabajo, sin embargo, este trabajo aparece como otra publicación más (y de otro autor). Si es el software mencionado no es parte de las contribuciones del trabajo, por favor, eliminar estas frases.  Otros comentarios:  * En la sección del caso de estudio no es tan fácil entenderla. Por ejemplo, figuras 2, 5 y 7 no se entiende a que se refiere sus siglas (ej. AMS). Supongo que corresponden a los sub-propiedades.  * Se recomienda intercambiar la sección 3 por la sección 2. Es decir, primero explicar que es un agente y luego hablar del trabajo relacionado.  * No me queda claro si es "agente software" o "agente de software". Incluso, el autor también confunde esta terminología (primera oración de la sección 4).

A:
es
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