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].

El artículo aborda un tema de interés como lo es el manejo de grandes datos y su estructura en base a metadatos para su procesamiento en la era de la web semántica.  El artículo presenta serias deficiencias léxicas e incluso semánticas. La traducción del título y el abstract en inglés presentan errores graves. En el aspecto semántico, en la introducción mencionan como problema que las bases de datos relacionales no tengan problemas de adaptación. Como error léxico, escriben A un, en lugar de Aún. En forma errónea se utiliza el término Apropósito, en lugar de A propósito. Las conclusiones no permiten centrar una propuesta. Hay buena utilización de las referencias bibliográficas.
es

El artículo describe el armado de un robot para docencia de pregrado, con la información de ecuaciones y control para su utilización. No presenta un trabajo de investigación específico, pero es una buena alternativa para ser mostrado en el congreso. Algunos comentarios de carácter general, que deben ser modificado para su aceptación final: 1.- Por las características de la comunicación, se entiende las pocas referencias que presenta. 2.- Las ecuaciones fueron copiadas y pegadas, en la mayoría de las veces, no se ven muy legibles, por lo que se aconseja reescribirlas con un editor de ecuaciones todas. 3.- Las figuras no presentan fuentes o referencias , de donde fueron extraídas. 4.- Existen párrafos largos que pueden acomodarse mejor con una puntuación.  En general es un trabajo bien escrito, se entiende fácilmente su contenido, su presentación con los "copy paste" atentan.
es

This paper proposes a neural network architecture that represent structural linguistic knowledge in a memory network for sequence tagging tasks (in particular, slot-filling of the natural language understanding unit in conversation systems). Substructures (e.g. a node in the parse tree) is encoded as a vector (a memory slot) and a weighted sum of the substructure embeddings are fed in a RNN at each time step as additional context for labeling.  -----Strengths-----  I think the main contribution of this paper is a simple way to "flatten" structured information to an array of vectors (the memory), which is then connected to the tagger as additional knowledge. The idea is similar to structured / syntax-based attention (i.e. attention over nodes from treeLSTM); related work includes Zhao et al on textual entailment, Liu et al. on natural language inference, and Eriguchi et al. for machine translation. The proposed substructure encoder is similar to DCNN (Ma et al.): each node is embedded from a sequence of ancestor words. The architecture does not look entirely novel, but I kind of like the simple and practical approach compared to prior work.  -----Weaknesses-----  I'm not very convinced by the empirical results, mostly due to the lack of details of the baselines. Comments below are ranked by decreasing importance.  -  The proposed model has two main parts: sentence embedding and substructure embedding. In Table 1, the baseline models are TreeRNN and DCNN, they are originally used for sentence embedding but one can easily take the node/substructure embedding from them too. It's not clear how they are used to compute the two parts.  - The model uses two RNNs: a chain-based one and a knowledge guided one. The only difference in the knowledge-guided RNN is the addition of a "knowledge" vector from the memory in the RNN input (Eqn 5 and 8). It seems completely unnecessary to me to have separate weights for the two RNNs. The only advantage of using two is an increase of model capacity, i.e. more parameters. Furthermore, what are the hyper-parameters / size of the baseline neural networks? They should have comparable numbers of parameters.  - I also think it is reasonable to include a baseline that just input additional knowledge as features to the RNN, e.g. the head of each word, NER results etc.  - Any comments / results on the model's sensitivity to parser errors?  Comments on the model:  - After computing the substructure embeddings, it seems very natural to compute an attention over them at each word. Is there any reason to use a static attention for all words? I guess as it is, the "knowledge" is acting more like a filter to mark important words. Then it is reasonable to include the baseline suggest above, i.e. input additional features.  - Since the weight on a word is computed by inner product of the sentence embedding and the substructure embedding, and the two embeddings are computed by the same RNN/CNN, doesn't it means nodes / phrases similar to the whole sentence gets higher weights, i.e. all leaf nodes?  - The paper claims the model generalizes to different knowledge but I think the substructure has to be represented as a sequence of words, e.g. it doesn't seem straightforward for me to use constituent parse as knowledge here.  Finally, I'm hesitating to call it "knowledge". This is misleading as usually it is used to refer to world / external knowledge such as a knowledge base of entities, whereas here it is really just syntax, or arguably semantics if AMR parsing is used.  -----General Discussion-----  This paper proposes a practical model which seems working well on one dataset, but the main ideas are not very novel (see comments in Strengths). I think as an ACL paper there should be more takeaways. More importantly, the experiments are not convincing as it is presented now. Will need some clarification to better judge the results.  -----Post-rebuttal-----  The authors did not address my main concern, which is whether the baselines (e.g. TreeRNN) are used to compute substructure embeddings independent of the sentence embedding and the joint tagger. Another major concern is the use of two separate RNNs which gives the proposed model more parameters than the baselines. Therefore I'm not changing my scores.

en