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 Q]: This paper proposes a method for evaluating topic quality based on using word embeddings to calculate similarity (either directly or indirectly via matrix factorisation), achieving impressive results over standard datasets.  The proposed method represents a natural but important next step in the evolutionary path of research on topic evaluation. The thing that troubled me most with the results was that, while you achieve state-of-the-art results for all three datasets, there are large inconsistencies in which methods perform and which methods perform less well (below the state of the art). In practice, none of the proposed methods consistently beats the state of the art, and the SVD-based methods perform notably badly over the genomics dataset. For someone who wants to take your method off the shelf and use it over any arbitrary dataset, this is a considerable worry. I suspect that the lower results for SVD over genomics relate to the proportion of OOV terms (see comment below), and that it may be possible to automatically predict which method will perform best based on vocab match with GloVe etc., but there is no such discussion in the paper.  Other issues:  - the proposed method has strong similarities with methods proposed in the   lexical chaining literature, which I would encourage the authors to read up   on and include in any future version of the paper  - you emphasis that your method has no parameters, but the word embedding   methods have a large number of parameters, which are implicit in your   method. Not a huge deal, but worth acknowledging  - how does your method deal with OOV terms, e.g. in the genomics dataset   (i.e. terms not present in the pretrained GloVe embeddings)? Are they simply   ignored? What impact does this have on the method?  Low-level issues:  - in your description of word embeddings in Section 2.1, you implicitly assume   that the length of the vector is unimportant (in saying that cosine   similarity can be used to measure the similarity between two vectors). If   the vectors are unit length, this is unproblematic, but word2vec actually   doesn't return unit-length vectors (the pre-trained vectors have been   normalised post hoc, and if you run word2vec yourself, the vector length is   certainly not uniform). A small detail, but important.  - the graphs in Figure 1 are too small to be readable
[EX A]: en

[EX Q]: This paper proposes joint CTC-attention end-to-end ASR, which utilizes both advantages in training and decoding.   - Strengths: It provides a solid work of hybrid CTC-attention framework in training and decoding, and the experimental results showed that the proposed method could provide an improvement in Japanese CSJ and Mandarin Chinese telephone speech recognition task.   - Weaknesses: The only problem is that the paper sounds too similar with Ref [Kim et al., 2016] which will be officially published in the coming IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2017. Kim at al., 2016, proposes joint CTC-attention using MTL for English ASR task, and this paper proposes joint CTC-attention using MTL+joint decoding for Japanese and Chinese ASR tasks. I guess the difference is on joint decoding and the application to Japanese/Chinese ASR tasks. However, the difference is not clearly explained by the authors. So it took sometimes to figure out the original contribution of this paper.  (a) Title:  The title in Ref [Kim et al., 2016] is “Joint CTC- Attention Based End-to-End Speech Recognition Using Multi-task Learning”, while the title of this paper is “Joint CTC-attention End-to-end Speech Recognition”. I think the title is too general. If this is the first paper about "Joint CTC-attention" than it is absolutely OK. Or if Ref [Kim et al., 2016] will remain only as pre-published arXiv, then it might be still acceptable. But since [Kim et al., 2016] will officially publish in IEEE conference, much earlier than this paper, then a more specified title that represents the main contribution of this paper in contrast with the existing publication would be necessary.   (b) Introduction: The author claims that “We propose to take advantage of the constrained CTC alignment in a hybrid CTC-attention based system. During training, we attach a CTC objective to an attention-based encoder network as a regularization, as proposed by [Kim at al., 2016].“ Taking advantage of the constrained CTC alignment in a hybrid CTC-attention is the original idea from [Kim at al., 2016]. So the whole argument about attention-based end-to-end ASR versus CTC-based ASR, and the necessary of CTC-attention combination is not novel. Furthermore, the statement “we propose … as proposed by [Kim et al, 2016]” is somewhat weird. We can build upon someone proposal with additional extensions, but not just re-propose other people's proposal. Therefore, what would be important here is to state clearly the original contribution of this paper and the position of the proposed method with respect to existing literature  (c) Experimental Results: Kim at al., 2016 applied the proposed method on English task, while this paper applied the proposed method on Japanese and Mandarin Chinese tasks. I think it would be interesting if the paper could explain in more details about the specific problems in Japanese and Mandarin Chinese tasks that may not appear in English task. For example, how the system could address multiple possible outputs. i.e., Kanji, Hiragana, and Katakana given Japanese speech input without using any linguistic resources. This could be one of the important contributions from this paper.  - General Discussion: I think it would be better to cite Ref [Kim et al., 2016] from the official IEEE ICASSP conference, rather than pre-published arXiv: Kim, S., Hori, T., Watanabe, S., "Joint CTC- Attention Based End-to-End Speech Recognition Using Multi-task Learning", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2017, pp. to appear.
[EX A]: en

[EX Q]: This paper presents a Stack LSTM parser based on the work of Henderson et al. (2008, 2013) on joint syntactic/semantic transition-based parsing and Dyer et al. (2015) on stack LSTM syntactic parsing. The use of the transition system from the former and the stack LSTM from the latter shows interesting results compared to the joint systems on the CoNLL 2008 and 2009 shared tasks.  I like this paper a lot because it is well-written, well-explained, the related work is good and the results are very interesting. The methodology is sound (with a minor concern regarding the Chinese embeddings, leading me to believe than very good embeddings can be more informative than a very clever model...).  Moreover, the description of the system is clear, the hyperparameters are justified and the discussion is interesting.  The only thing I would say is that the proposed system lacks originality in the sense that the work of Henderson et al. puts the basis of semi-synchronised joint syntax-semantic transition-based parsing several years ago and Dyer et al. came up with the stack LSTM last year, so it is not a new method, per say. But in my opinion, we were waiting for such a parser to be designed and so I'm glad it was done here.
[EX A]:
en