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:
The paper presents a general approach to modeling for natural language understanding problems with two distinct textual inputs (such as a question and a source text) that can be aligned in some way. In the approach, soft attention is first used to derive alignments between the tokens of the two texts, then a comparison function uses the resulting alignments (represented as pairs of attention queries and attention results) to derive a representations that are aggregated by CNN into a single vector from which an output can be computed. The paper both presents this as an overall modeling strategy that can be made to work quite well, and offers a detailed empirical analysis of the comparison component of the model.  This work is timely. Language understanding problems of this kind are a major open issue in NLP, and are just at the threshold of being addressable with representation learning methods. The work presents a general approach which is straightforward and reasonable, and shows that it can yield good results. The work borders on incremental (relative to their earlier work or that of Parikh et al.), but it contributes in enough substantial ways that I'd strongly recommend acceptance.  Detail:  - The model, at least as implemented for the problems with longer sequences (everything but SNLI), is not sensitive to word order. It is empirically competitive, but this insensitivity places a strong upper bound on its performance. The paper does make this clear, but it seems salient enough to warrant a brief mention in the introduction or discussion sections. - If I understand correctly, your attention strategy is based more closely on the general/bilinear strategy of Luong et al. '15 than it is on the earlier Bahdanau work. You should probably cite the former (or some other more directly relevant reference for that strategy). - Since the NTN risks overfitting because of its large number of parameters, did you try using a version with input dimension l and a smaller output dimension m (so an l*l*m tensor)? - You should probably note that SubMultNN looks a lot like the strategy for *sentence*-level matching in the Lili Mou paper you cite. - Is there a reason you use the same parameters for preprocessing the question and answer in (1)? These could require different things to be weighted highly.
answer:
en


question:
El artículo, según los revisores, no es una contribución científica, pero el tema resulta particularmente interesante de discutir en nuestro congreso. Es por ello que se recomienda aceptar el artículo para su presentación en el Workshop de aplicaciones empresariales en lugar de paper regular.
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