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].

Ex Input:
The authors of this work propose a learnable approach to reducing the dimensionality of learned filters in deep neural networks. This is an interesting approach, but the presented work looks a bit raw. 1. There are many typos in this manuscript. 2. The experimental results are rather weak and don't show much improvement in accuracy. Instead the authors could position this work as a compression mechanism and would have to compare to low rank approximation of filters for DNNs. Yet this is not done. 3. Aside from compression, OMG can be viewed as a form of regularization to reduce the unnecessary capacity of the network to improve generalization. Again, this is not addressed in enough detail. 4. If the authors care to compare their approach to other 1-shot learning methods, then they would have to evaluate their approach with siamese and triplet learning networks. This isn't done.

Ex Output:
en

Ex Input:
- 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.

Ex Output:
en

Ex Input:
El articulo presenta un estudio de la percepcion que tienen los ingenieros de requisitos (noveles y expertos) en relacion a la efectividad de las diferentes tecnicas de educcion para el descubrimiento de los requisitos en un proyecto de desarrollo de software. El articulo trata un tema que merece cierta atencion ante la gran cantidad de tecnicas de educcion que existen y la desorientacion de los ingenieros de requisitos respecto de cual utilizar en un determinado contexto. El estudio realizado considera como noveles Ingenieros que no tienen conocimiento de tecnicas de educcion y uno de los atributos que debe ser calificado como base del estudio comparativo, es precisamente su experiencia. De hecho como conclusion se expresa que "una mas amplia formacion y principalmente de tipo practica, es necesaria para que los ingenieros noveles puedan decidir.", eso es de Perogrullo. Deberia ser considera una gama mayor de atributos del contexto como los sugeridos en [14], que permitan conclusiones mas veraces.

Ex Output:
es