TASK DEFINITION: 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].
PROBLEM: This paper proposed the group sparse auto-encoder for feature extraction. The author then stack the group sparse auto-encoders on top of CNNs to extract better question sentence representation for QA tasks.   Pros:  - group-sparse auto-encoder seems new to me. - extensive experiments on QA tasks.   Cons: - The idea is somewhat incremental. - Writing need to be improved.  - Lack of ablation studies to show the effectiveness of the proposed approach.   Moreover, I am not convinced by the author's answer regarding the baseline. A separate training stages of CNN+SGL for comparison is fine. The purpose is to validate and analyze why the proposed SGA is preferred rather than group lasso, e.g. joint training could improve, or the proposed group-sparse regularization outperforms l_21 norm, etc. However, we can't see it from the current experiments.

SOLUTION: en

PROBLEM: The paper describes a novel technique to improve the efficiency of computation graphs in deep learning frameworks. An impressive speedup can be observed in their implementation within TensorFlow. The content is presented with sufficient clarity, although some more graphical illustrations could be useful. This work is relevant in order to achieve highest performance in neural network training.   Pros:  - significant speed improvements through dynamic batching - source code provided   Cons:  - the effect on a large real-world (ASR, SMT) would allow the reader to put the improvements better into context - presentation/vizualisation can be improved

SOLUTION: en

PROBLEM: - Strengths:  Improves over the state-of-the-art. Method might be applicable for other domains.  - Weaknesses:  Not much novelty in method.  Not quite clear if data set is general enough for other domains.  - General Discussion:  This paper describes a rule-based method for generating additional weakly labeled data for event extraction.  The method has three main stages.  First, it uses Freebase to find important slot fillers for matching sentences in Wikipedia (using all slot fillers is too stringent resulting in too few matches).  Next, it uses FrameNet to to improve reliability of labeling trigger verbs and to find nominal triggers.  Lastly, it uses a multi-instance learning to deal with the noisily generated training data.  What I like about this paper is that it improves over the state-of-the-art on a non-trival benchmark.  The rules involved don't seem too obfuscated, so I think it might be useful for the practitioner who is interested to improve IE systems for other domains.  On the other hand, some some manual effort is still needed, for example for mapping Freebase event types to ACE event types (as written in Section 5.3 line 578).  This also makes it difficult for future work to calibrate apple-to-apple against this paper.              Apart from this, the method also doesn't seem too novel.  Other comments:  - I'm also concern with the generalizability of this method to other   domains.  Section 2 line 262 says that 21 event types are selected   from Freebase.  How are they selected?  What is the coverage on the 33 event types in the ACE data.  - The paper is generally well-written although I have some   suggestions for improvement.              Section 3.1 line 316 uses "arguments liked time, location...".  If you mean roles or arguments, or maybe you want to use actual realizations of time and location as examples.  There are minor typos, for e.g. line 357 is missing a "that", but this is not a major concern I have for this paper.

SOLUTION:
en