instruction:
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].
question:
El artículo tiene como objetivo brindar una arquitectura de referencia y solución al problema de integración e interoperabilidad entre sistemas de salud.   Algunas observaciones son: - Falta nombre del paper en español - Cambiar "Index Terms" por "keywords" - Falta Resumen y Palabras Clave en español - No usar color en figuras (según formato) - Poner acento a "se diseño" - Falta una sección de Conclusiones
answer:
es


question:
SYNOPSIS: The paper proposes a new neural network-based model for reading comprehension (reading a passage of text and answering questions based on the passage). It is similar in spirit to several other recent models, with the main exception that it is able to predict answers of different lengths, as opposed to single words/tokens/entities. The authors compare their model on the Stanford Question Answering Dataset (SQuAD), and show improvements over the baselines, while apparently lagging quite far behind the current state of the art reported on the SQuAD leaderboard.  THOUGHTS: The main novelty of the method is to be able to identify phrases of different lengths as possible answers to the question. However, both approaches considered -- using a POS pattern trie tree to filter out word sequences with POS tags matching those of answers in the training set, and brute-force enumeration of all phrases up to length N -- seem somewhat orthogonal to the idea of "learning end-to-end " an answer chunk extraction model. Furthermore, as other reviews have pointed out, it seems that the linguistic features actually contribute a lot to the final accuracy (Table 3). One could argue that these are easy to obtain using standard taggers, but it takes away even more from the idea of an "end-to-end trained" system.  The paper is generally well written, but there are several crucial sections in parts describing the model where it was really hard for me to follow the descriptions. In particular, the attention mechanism seems fairly standard to me in a seq2seq sense (i.e. there is nothing architecturally novel about it, as is for instance the case with the Gated Attentive Reader). I may be missing something, but even after the clarification round I still don't understand how it is novel compared to standard attention used in for instance seq2seq models.  Finally, although the method is shown to outperform the baseline method reported in the original paper introducing the SQuAD dataset, it currently seems to be 12th (out of 15 systems) on the leaderboard (
answer:
en


question:
- Strengths:  -- A well-motivated approach, with a clear description and solid results.  - Weaknesses:  -- Nothing substantial other than the comments below.   - General Discussion:  The paper describes a new method called attention-over-attention for reading comprehension. First layers of the network compute a vector for each query word and document word, resulting in a |Q|xK matrix for the query and a |D|xK for the document. Since the answer is a document word, an attention mechanism is used for assigning weights to each word, depending on their interaction with query words. In this work, the authors deepen a traditional attention mechanism by computing a weight for each query word through a separate attention and then using that to weight the main attention over document words. Evaluation is properly conducted on benchmark datasets, and various insights are presented through an analysis of the results as well as a comparison to prior work. I think this is a solid piece of work on an important problem, and the method is well-motivated and clearly described, so that researchers can easily reproduce results and apply the same techniques to other similar tasks.  - Other remarks:  -- p4, Equation 12: I am assuming i is iterating over training set and p(w) is referring to P(w|D,Q) in the previous equation? Please clarify to avoid confusion.  -- I am wondering whether you explored/discussed initializing word embeddings with existing vectors such as Google News or Glove? Is there a reason to believe the general-purpose word semantics would not be useful in this task?  -- p6 L589-592: It is not clear what the authors are referring to when they say 'letting the model explicitly learn weights between individual attentions'? Is this referring to their own architecture, more specifically the GRU output indirectly affecting how much attention will be applied to each query and document word? Clarifying that would be useful. Also, I think the improvement on validation is not 4.1, rather 4.0 (72.2-68.2).  -- p7 Table 5: why do you think the weight for local LM is relatively higher for the CN task while the benefit of adding it is less? Since you included the table, I think it'll be nice to provide some insights to the reader.  -- I would have liked to see the software released as part of this submission.  -- Typo p2 L162 right column: "is not that effective than expected" --> "is not as effective as expected"?  -- Typo p7 L689 right column: "appear much frequent" --> "appears more frequently"?  -- Typo p8 L719-721 left column: "the model is hard to" --> "it is hard for the model to"? & "hard to made" --> "hard to make"?
answer:

en