Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 3: Weak Reject
review_content: The paper proposed a word embedding model to incorporate the sentiment information. The paper provided both maximum likelihood estimation and maximum posterior estimation for the proposed framework. Improved experiment results on word similarity and low frequency embeddings are presented. Overall, the paper incorporates the sentiment information in a neat way. And my main concern is the around the Bayesian inference and the prior knowledge distilled into the model.  Detail comments are as following, 

1. The model employed Laplace approximation for posterior distribution. Not quite sure this is a good idea for the Bernoulli case since Laplace approximation is trying to use Gaussian distribution to approximate the region around the mode. How will the MAP solution compare with a full Bayesian solution such as VB or sampling-based methods?

2. Another concern is the prior introduced into the model. Normally prior information will be washed away as the training data grow. Not the case for the low frequency examples that the model performed well on. Would it possible that the improved performance on low frequency example is just a side effect of the biased introduced by the prior? How sensitive will the embedding perform with respect to the prior selected?

review_title: Official Blind Review #3

final_score: rating: 1: Reject
review_content: 
This paper proposes a method to learn word embedding by incorporating additional sentiment information. The proposed method extends from D-GloVe by adding the probability of positive sentiment to the loss function. The paper presents three experiments: word similarity, word-level sentiment analysis, and sentence-level sentiment analysis. The experiments show that the method performs comparably with other baseline methods and outperforms in the low-frequency sentence setting (i.e. sentence containing lower frequency words).

I recommend rejecting this paper because (1) the writing is unclear and hard to follow, and (2) the experiment results are not convincing.

From what I can understand in the model part, there are many clarifications needed, not to mention the writing style. I think the re-derivations of GloVe and D-GloVe are not helpful as they cloud the main contribution of the paper. The author should clearly highlight the differences between the main subjects of the experiment: MLESWE and BESWE. In addition, it is not clearly motivated why we need Dirichlet prior for the sentiment variable.

While the claim is to learn better embeddings for rare words, the experiments show that the proposed methods have similar results to the previous work. The only gain we can observe is in the sentence-level experiments in which other factors could affect the performance. Thus, it is hard to draw a supportive conclusion.

Finally, the writing quality must be improved. The paper contains a lot of unrelated and redundant texts (it could be that I could follow the paper). 
1. I do not think eq 2 is a representative of how the paper train the model, nor attempt to compare with. 

2. As mention earlier, in section 3.2 and 3.3, the re-derivation is not particularly helpful. I think the paper should put more emphasis on the novelty of the work. 

3. Plots in the experiment results are illegible. Tables should be more suitable for Figures 1, 2, and 3. 

I urged the authors to revise this paper and make sure it follows the formatting guideline, especially the citations. Finally, I'd recommend the authors have a professional writer (English) review the paper before submission.

review_title: Official Blind Review #2

final_score: rating: 6: Weak Accept
review_content: The paper aims at extending GloVe word embedding model so that the resulting embeddings should capture sentiments (e.g. "good" is positive while "bad" is negative). The key idea is to employ an extension term to deal with the fact that some words appearing in text with sentiment information. Furthermore, to deal with the fact that many words an infrequent, besides maximum likelihood estimation, the paper proposes to use bayesian estimation. In the experiments, Stanford sentiment tree (SST) corpus is used. The word embeddings from the two models (each trained on different estimation methods) show their capability of expressing sentiments, compared with popular methods like Glove, word2vec. 

I would accept this paper because: 
- This paper is well written, with thoughtful maths details. 
- The proposed models, although are extensions of GloVe, gives interesting (and rigorous) points of how to add sentiment information. 
- The experiments do support what the paper claims.

I would reject it because of the experiments. The dataset (SST) is so small and thus is questionable about the quality of the learned word embeddings and the comparisons. I think there should be better ways, such as word embeddings are trained on massive data (like for GloVe and word2vec), then are fine-tuned on sentiment analysis dataset. Also, I was wondering whether there's a way to collect more sentiment data (like in SE-HyRank paper).


review_title: Official Blind Review #1

Meta-review: This paper proposes a method to improve word embedding by incorporating sentiment probabilities. Reviewer appreciate the interesting and simple approach and acknowledges improved results in low-frequency words.

However, reviewers find that the paper is lacking in two major aspects:
1) Writing is unclear, and thus it is difficult to understand and judge the contributions of this research.
2) Perhaps because of 1, it is not convincing that the improvements are significant and directly resulting from the modeling contributions.

I thank the authors for submitting this work to ICLR, and I hope that the reviewers' comments are helpful in improving this research for future submission.
Decision: Reject