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].

Example Input: Ese trabajo muestra el procedimiento propuesto para la detección automática de fracturas en pozos. La metodología se ve novedosa e interesante. El artículo muestra bien la metodología y tiene una buena redacción, pero al finalizar el artículo falta una mejor presentación de los resultados en pozos reales.  No queda claro el aporte del artículo, primero indica que es la detección automática y en otra parte indica que es la modificación al algoritmo.  Debe mejorar la presentación de los resultados en pozos reales y compararlo con lo deseado y quizás con resultados de aplicar otras técnicas.
Example Output: es

Example Input: 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.
Example Output: en

Example Input: Thanks for the response. I look forward to reading about the effect of incentives and the ambiguity of the language in the domain.  Review before author response: The paper proposes a way to build natural language interfaces by allowing a set of users to define new concepts and syntax. It's an (non-trivial) extension of S. I. Wang, P. Liang, and C. Manning. 2016. Learning language games through interaction  Questions: - What is the size of the vocabulary used  - Is it possible to position this paper with respect to previous work on inverse reinforcement learning and imitation learning ?  Strengths: - The paper is well written - It provides a compelling direction/solution to the problem of dealing with a large set of possible programs while learning natural language interfaces.   Weaknesses: - The authors should discuss the effect of the incentives on the final performance ? Were other alternatives considered ?  - While the paper claims that the method can be extended to more practical domains, it is not clear to me how straightforward it is going to be. How sensitive is the method to the size of the vocabulary required in a domain ? Would increased ambiguity in natural language create new problems ? These questions are not discussed in the current experiments. - A real-world application would definitely strengthen the paper even more.
Example Output:
en