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].

[Q]: This paper combines variational RNN (VRNN) and domain adversarial networks (DANN) for domain adaptation in the sequence modelling domain.  The VRNN is used to learn representations for sequential data, which is the hidden states of the last time step.  The DANN is used to make the representations domain invariant, therefore achieving cross domain adaptation.  Experiments are done on a number of data sets, and the proposed method (VRADA) outperforms baselines including DANN, VFAE and R-DANN on almost all of them.  I don't have questions about the proposed model, the model is quite clear and seems to be a simple combination of VRNN and DANN.  But a few questions came up during the pre-review question phase:  - As the authors have mentioned, DANN in general outperforms MMD based methods, however, the VFAE method which is based on MMD regularization on the representations seems to outperform DANN across the board.  That seems to indicate VRNN + MMD should also be a good combination.  - One baseline the authors showed in the experiments is R-DANN, which is an RNN version of DANN.  There are two differences between R-DANN and VRADA: (1) R-DANN uses deterministic RNN for representation learning, while VRADA uses variational RNN; (2) on target domain R-DANN only optimizes adversarial loss, while VRADA optimizes both adversarial loss and reconstruction loss for feature learning.  It would be good to analyze further where the performance gain comes from.
[A]: en


[Q]: Considera el uso de tecnologías móviles para incorporar en la medición de material particulado que existe en el interior de una mina. Será interesante ver las ventajas y desventajas de esta aplicación con otras existentes.  Es un estudio solamente. Todavía no hay resultados.
[A]: es


[Q]: This work combines a LDA-type topic model with a RNN and models this by having an additive effect on the predictive distribution via the topic parameters. A variational auto-encoder is used to infer the topic distribution for a given piece of text and the RNN is trained as a RNNLM. The last hidden state of the RNNLM and the topic parameters are then concatenated to use as a feature representation.  The paper is well written and easy to understand. Using the topic as an additive effect on the vocabulary allows for easy inference but intuitively I would expect the topic to affect the dynamics too, e.g. the state of the RNN. The results on using this model as a feature extractor for IMDB are quite strong. Is the RNN fine-tuned on the labelled IMDB data? However, the results for PTB are weaker. From the original paper, an ensemble of 2 LSTMs is able to match the topicRNN score. This method of jointly modelling topics and a language model seems effective and relatively easy to implement.  Finally, the IMDB result is no longer state of the art since this result appeared in May (Miyato et al., Adversarial Training Methods for Semi-Supervised Text Classification).  Some questions: How important is the stop word modelling? What do the results look like if l_t = 0.5 for all t?  It seems surprising that the RNN was more effective than the LSTM. Was gradient clipping tried in the topicLSTM case? Do GRUs also fail to work?  It is also unfortunate that the model requires a stop-word list. Is the link in footnote 4 the one that is used in the experiments?  Does factoring out the topics in this way allow the RNN to scale better with more neurons? How reasonable does the topic distribution look for individual documents? How peaked do they tend to be? Can you show some examples of the inferred distribution? The topics look odd for IMDB with the top word of two of the topics being the same: 'campbell'. It would be interesting to compare these topics with those inferred by LDA on the same datasets.  Minor comments: Below figure 2: GHz -> GB \Gamma is not defined.
[A]:
en