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 Input:
El trabajo me ha resultado interesante y me ha parecido un artículo muy atractivo de leer. Sin embargo, mi principal cuestión por la que he aconsejado su rechazo es porque realmente no considero que lo presentado sea investigación. El artículo presenta una experiencia en el uso de un aplicativo móvil para el pago de impuestos pero esto es algo que ya lleva ejecutándose en otros entornos. ¿Qué aporta esto de nuevo? Igual existe alguna novedad respecto a este desarrollo pero no ha quedado reflejada en el paper. Incluso, los planteamientos de trabajos futuros de investigación planteados, yo los entiendo como evolutivos o nuevas aplicaciones. El paper sería adecuado para una track section pero no para un congreso de investigación bajo mi punto de vista.

Ex Output:
es


Ex Input:
While I understand the difficulty of collecting audio data from animals, I think this type of feature engineering does not go in the right direction. I would rather see a model than learns the feature representation from data.  I would think it should be possible to collect a more substantial corpus in zoos / nature etc, and then train a generative model. The underlying learned feature representation could be then used to feed a classifier. I'm not familiar with the particularities of this task, it's hard to judge the improvements by using chirplets.

Ex Output:
en


Ex Input:
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 Output:

en