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 gives a theoretical motivation for tieing the word embedding and output projection matrices in RNN LMs. The argument uses an augmented loss function which spreads the output probability mass among words with close word-embedding.   I see two main drawbacks from this framework: The augmented loss function has no trainable parameters and is used for only for regularization. This is not expected to give gains with large enough datasets.  The augmented loss is heavily “engineered” to produce the desired result of parameter tying. It’s not clear what happens if you try to relax it a bit, by adding parameters, or estimating y~ in a different way.   Nevertheless the argument is very interesting, and clearly written. The simulated results indeed validate the argument, and the PTB results seem promising.  Minor comments: Section 3: Can you clarify if y~ is conditioned on the t example or on the entire history. Eq. 3.5: i is enumerated over V (not |V|)

A: en
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Q: In this paper, the authors propose a new method to learn hierarchical representations of sentences, based on reinforcement learning. They propose to learn a neural shift-reduce parser, such that the induced tree structures lead to good performance on a downstream task. They use reinforcement learning (more specifically, the policy gradient method REINFORCE) to learn their model. The reward of the algorithm is the evaluation metric of the downstream task. The authors compare two settings, (1) no structure information is given (hence, the only supervision comes from the downstream task) and (2) actions from an external parser is used as supervision to train the policy network, in addition to the supervision from the downstream task. The proposed approach is evaluated on four tasks: sentiment analysis, semantic relatedness, textual entailment and sentence generation.  I like the idea of learning tree representations of text which are useful for a downstream task. The paper is clear and well written. However, I am not convinced by the experimental results presented in the paper. Indeed, on most tasks, the proposed model is far from state-of-the-art models:  - sentiment analysis, 86.5 v.s. 89.7 (accuracy);  - semantic relatedness, 0.32 v.s. 0.25 (MSE);  - textual entailment, 80.5 v.s. 84.6 (accuracy). From the results presented in the paper, it is hard to know if these results are due to the model, or because of the reinforcement learning algorithm.  PROS:  - interesting idea: learning structures of sentences adapted for a downstream task.  - well written paper. CONS:  - weak experimental results (do not really support the claim of the authors).  Minor comments: In the second paragraph of the introduction, one might argue that bag-of-words is also a predominant approach to represent sentences. Paragraph titles (e.g. in section 3.2) should have a period at the end.  ---------------------------------------------------------------------------------------------------------------------- UPDATE  I am still not convinced by the results presented in the paper, and in particular by the fact that one must combine the words in a different way than left-to-right to obtain state of the art results. However, I do agree that this is an interesting research direction, and that the results presented in the paper are promising. I am thus updating my score from 5 to 6.

A: en
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Q: - Strengths: 1. The idea of assigning variable-length document segments with dependent topics is novel. This prior knowledge is worth incorporated in the LDA-based framework. 2. Whereas we do not have full knowledge on recent LDA literature, we find the part of related work quite convincing. 3. The method proposed for segment sampling with O(M) complexity is impressive. It is crucial for efficient computation.   - Weaknesses: 1. Compared to Balikas COLING16's work, the paper has a weaker visualization (Fig 5), which makes us doubt about the actual segmenting and assigning results of document. It could be more convincing to give a longer exemplar and make color assignment consistent with topics listed in Figure 4. 2. Since the model is more flexible than that of Balikas COLING16, it may be underfitting, could you please explain this more?  - General Discussion: The paper is well written and structured. The intuition introduced in the Abstract and again exemplified in the Introduction is quite convincing. The experiments are of a full range, solid, and achieves better quantitative results against previous works. If the visualization part is stronger, or explained why less powerful visualization, it will be more confident. Another concern is about computation efficiency, since the seminal LDA work proposed to use Variational Inference which is faster during training compared to MCMC, we wish to see the author’s future development.

A:
en
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