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:
This submission proposes to learn the word decomposition, or word to sub-word sequence mapping jointly with the attention based sequence-to-sequence model. A particular feature of this approach is that the decomposition is not static, instead, it also conditions on the acoustic input, and the mapping is probabilistic, i.e., one word may map to multiple sub-word sequences. The authors argue that the dynamic decomposition approach can more naturally reflect the acoustic pattern. Interestingly, the motivation behind this approach is analogous to learning the pronunciation mixture model for HMM based speech recognition, where the probabilistic mapping from a word to its pronunciations also conditions on the acoustic input, e.g.,  I. McGraw, I. Badr, and J. Glass, "Learning lexicons form speech using a pronunciation mixture model," in IEEE Transactions on Audio, Speech, and Language Processing, 2013  L. Lu, A. Ghoshal, S. Renals, "Acoustic data-driven pronunciation lexicon for large vocabulary speech recognition", in Proc. ASRU   R. Singh, B. Raj, and R. Stern, "Automatic generation of subword units for speech recognition systems,"  in IEEE Transactions on Speech and Audio Processing, 2002  It would be interesting to put this work in the context by linking it to some previous works in the HMM framework.  Overall, the paper is well written, and it is theoretically convincing. The experimental study could be more solid, e.g., it is reasonable to have a word-level baseline, as the proposed approach lies in between the character-level and word-level systems. the vocabulary size of the WSJ si284 dataset is 20K at maximum, which is not very large for the softmax layer, and it is a closed vocabulary task. I guess the word-level system may be also competitive to the numbers reported in this paper. Furthermore, can you explain what is the computational bottleneck of the proposed approach? You downsampled the data by the factor of 4 using an RNN, and it still took around 5 days to converge. To me, it is a bit expensive, especially given that you only take one sample when computing the gradient. Table 2 is a little bit misleading, as CTC with language model and seq2seq with a language model model from Bahdanau et al. is much closer to the best number reported in this Table 2, while you may only get a very small improvement using a language model. Finally, "O(5) days to converge" sounds a bit odd to me.

Ex Output:
en


Ex Input:
This paper tackles the problem of compressing trained convnets with the goal of reducing memory overhead and speeding up the forward pass. As I understand it, the main contribution of this work is to develop fast convolution routines for sparse conv weights int he case of general sparsity (as compared with structured sparsity). They evaluate their method on both AlexNet and GoogLeNet as well as on various platforms. The authors make code available online. The paper is well written and does a good job of putting this work in the context of past model reduction techniques.  My main request of the authors would be to provide a concise summary of the speedup/memory gains achievable with this new work compared with previously published work. The authors do show the various sparsity level obtained with various methods of pruning but it is unclear to me how to translate the information given in the paper into an understanding of gains relative to other methods.

Ex Output:
en


Ex Input:
Se explica en forma ordenada y didáctica una experiencia de uso de TIC para la colaboración académica.  La originalidad del trabajo no es su dimensión fuerte, ver trabajos de larga data como Gorton et al. (1997) y actuales como Lanunile et al. (2010). Sin embargo, la relevancia del tema como patrón de referencia en actividades relacionadas a tesis científicas, asociada a su buena presentación,  hacen de él un aporte.  Se sugiere, en miras a una publicación en revista, que se compare la eficiencia y efectividad de las actividades sin el apoyo de estas herramientas versus la actual realidad (con herramientas). Ejemplos de preguntas posibles a responder de este análisis exploratorio: ¿Cuáles son las principales actividades donde el uso de herramientas colaborativas es clave? ¿Qué cambios en el diseño de estas herramientas se deberían desarrollar en el futuro? ¿Existen perfiles de estudiantes/académicos más propicios para la adopción de estas plataformas?  Referencias:  Gorton, I., Hawryszkiewycz, I., and Ragoonaden, K. 1997. Collaborative tools and processes to support software engineering shift work. BT Technology Journal 15, 3 (Jul. 1997), 189-198. Filippo Lanubile, Christof Ebert, Rafael Prikladnicki, Aurora Vizcaíno, "Collaboration Tools for Global Software Engineering," IEEE Software, vol. 27, no. 2, pp. 52-55, Mar./Apr. 2010.

Ex Output:

es