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 presents a system approach to combine multiple modalities to perform classification in a practical scenario (e-commerce).  In general, I find the proposed approach in the paper sound and solid, but do not see novelty in the paper: feature fusion and decision time fusion are both standard practices in multi-modal analysis, and the rest of the paper offers no surprise in implementing such approaches. This seems to be a better fit for venues that focus more on production systems, and seems to be a bad fit for ICLR where the focus is more on research of novel algorithms and theories.
[A]: en


[Q]: - Strengths:  The paper is well-written and easy to understand. The methods and results are interesting.  - Weaknesses:  The evaluation and the obtained results might be problematic (see my comments below).  - General Discussion:  This paper proposes a system for end-to-end argumentation mining using neural networks. The authors model the problem using two approaches: (1) sequence labeling (2) dependency parsing. The paper also includes the results of experimenting with a multitask learning setting for the sequence labeling approach. The paper clearly explains the motivation behind the proposed model. Existing methods are based on ILP, manual feature engineering and manual design of ILP constraints. However, the proposed model avoids such manual effort. Moreover, the model jointly learns the subtasks in argumentation mining and therefore, avoids the error back propagation problem in pipeline methods. Except a few missing details (mentioned below), the methods are explained clearly.  The experiments are substantial, the comparisons are performed properly, and the results are interesting. My main concern about this paper is the small size of the dataset and the large capacity of the used (Bi)LSTM-based recurrent neural networks (BLC and BLCC). The dataset includes only around 320 essays for training and 80 essays for testing. The size of the development set, however, is not mentioned in the paper (and also the supplementary materials). This is worrying because very few number of essays are left for training, which is a crucial problem. The total number of tags in the training data is probably only a few thousand. Compare it to the standard sequence labeling tasks, where hundreds of thousands (sometimes millions) of tags are available. For this reason, I am not sure if the model parameters are trained properly. The paper also does not analyze the overfitting problem. It would be interesting to see the training and development "loss" values during training (after each parameter update or after each epoch). The authors have also provided some information that can be seen as the evidence for overfitting: Line 622 "Our explanation is that taggers are simpler local models, and thus need less training data and are less prone to overfitting".  For the same reason, I am not sure if the models are stable enough. Mean and standard deviation of multiple runs (different initializations of parameters) need to be included. Statistical significance tests would also provide more information about the stability of the models and the reliability of results. Without these tests, it is hard to say if the better results are because of the superiority of the proposed method or chance.  I understand that the neural networks used for modeling the tasks use their regularization techniques. However, since the size of the dataset is too small, the authors need to pay more attention to the regularization methods. The paper does not mention regularization at all and the supplementary material only mentions briefly about the regularization in LSTM-ER. This problem needs to be addressed properly in the paper.  Instead of the current hyper-parameter optimization method (described in supplementary materials) consider using Bayesian optimization methods.  Also move the information about pre-trained word embeddings and the error analysis from the supplementary material to the paper. The extra one page should be enough for this.  Please include some inter-annotator agreement scores. The paper describing the dataset has some relevant information. This information would provide some insight about the performance of the systems and the available room for improvement.  Please consider illustrating figure 1 with different colors to make the quality better for black and white prints.  Edit:  Thanks for answering my questions. I have increased the recommendation score to 4. Please do include the F1-score ranges in your paper and also report mean and variance of different settings. I am still concerned about the model stability. For example, the large variance of Kiperwasser setting needs to be analyzed properly. Even the F1 changes in the range [0.56, 0.61] is relatively large. Including these score ranges in your paper helps replicating your work.
[A]: en


[Q]: In light of the authors' responsiveness and the updates to the manuscript -- in particular to clarify the meta-learning task -- I am updating my score to an 8.  -----  This manuscript proposes to tackle few-shot learning with neural networks by leveraging meta-learning, a classic idea that has seen a renaissance in the last 12 months. The authors formulate few-shot learning as a sequential meta-learning problem: each "example" includes a sequence of batches of "training" pairs, followed by a final "test" batch. The inputs at each "step" include the outputs of a "base learner" (e.g., training loss and gradients), as well as the base learner's current state (parameters). The paper applies an LSTM to this meta-learning problem, using the inner memory cells in the *second* layer to directly model the updated parameters of the base learner. In doing this, they note similarities between the respective update rules of LSTM memory cells and gradient descent. Updates to the LSTM meta-learner are computed based on the base learner's prediction loss for the final "test" batch. The authors make several simplifying assumptions, such as sharing weights across all second layer cells (analogous to using the same learning rate for all parameters). The paper recreates the Mini-ImageNet data set proposed in Vinyals et al 2016, and shows that the meta-learner LSTM is competitive with the current state-of-the-art (Matchin Networks, Vinyals 2016) on 1- and 5-shot learning.  Strengths: - It is intriguing -- and in hindsight, natural -- to cast the few-shot learning problem as a sequential (meta-)learning problem. While the authors did not originate the general idea of persisting learning across a series of learning problems, I think it is fair to say that they have advanced the state of the art, though I cannot confidently assert its novelty as I am not deeply familiar with recent work on meta-learning. - The proposed approach is competitive with and outperforms Vinyals 2016 in 1-shot and 5-shot Mini-ImageNet experiments. - The base learner in this setting (simple ConvNet classifier) is quite different from the nearest-neighbor-on-top-of-learned-embedding approach used in Vinyals 2016. It is always exciting when state-of-the-art results can be reported using very different approaches, rather than incremental follow-up work. - As far as I know, the insight about the relationship between the memory cell and gradient descent updates is novel here. It is interesting regardless. - The paper offers several practical insights about how to design and train an LSTM meta-learner, which should make it easier for others to replicate this work and apply these ideas to new problems. These include proper initialization, weight sharing across coordinates, and the importance of normalizing/rescaling the loss, gradient, and parameter inputs. Some of the insights have been previously described (the importance of simulating test conditions during meta-training; assuming independence between meta-learner and base learner parameters when taking gradients with respect to the meta-learner parameters), but the discussion here is useful nonetheless.  Weaknesses: - The writing is at times quite opaque. While it describes very interesting work, I would not call the paper an enjoyable read. It took me multiple passes (as well as consulting related work) to understand the general learning problem. The task description in Section 2 (Page 2) is very abstract and uses notation and language that is not common outside of this sub-area. The paper could benefit from a brief concrete example (based on MNIST is fine), perhaps paired with a diagram illustrating a sequence of few-shot learning tasks. This would definitely make it accessible to a wider audience. - Following up on that note, the precise nature of the N-class, few-shot learning problem here is unclear to me. Specifically, the Mini-ImageNet data set has 100 labels, of which 64/16/20 are used during meta-training/validation/testing. Does this mean that only 64/100 classes are observed through meta-training? Or does it mean that only 64/100 are observed in each batch, but on average all 100 are observed during meta-training? If it's the former, how many outputs does the softmax layer of the ConvNet base learner have during meta-training? 64 (only those observed in training) or 100 (of which 36 are never observed)? Many other details like these are unclear (see question). - The plots in Figure 2 are pretty uninformative in and of themselves, and the discussion section offers very little insight around them.  This is an interesting paper with convincing results. It seems like a fairly clear accept, but the presentation of the ideas and work therein could be improved. I will definitely raise my score if the writing is improved.
[A]:
en