Riemannian Optimization for Skip-Gram Negative Sampling
Skip-Gram Negative Sampling (SGNS) word embedding model, well known by its implementation in *word2vec* software, is usually optimized by stochastic gradient descent. It can be shown that optimizing for SGNS objective can be viewed as an optimization problem of searching for a good matrix with the low-rank constraint. The most standard way to solve this type of problems is to apply Riemannian optimization framework to optimize the SGNS objective over the manifold of required low-rank matrices. In this paper, we propose an algorithm that optimizes SGNS objective using Riemannian optimization and demonstrates its superiority over popular competitors, such as the original method to train SGNS and SVD over SPPMI matrix.
This paper presents a principled optimization method for SGNS (word2vec). While the proposed method is elegant from a theoretical perspective, I am not sure what the tangible benefits of this approach are. For example, does using Riemannian optimization allow the model to converge faster than the alternatives? The evaluation doesn*t show a dramatic advantage to RO-SGNS; the 1% difference on the word similarity benchmarks is within the range of hyperparameter effects (see *Improving Distributional Similarity with Lessons Learned from Word Embeddings*, (Levy et al., 2015)). The theoretical connection to Riemannian optimization is nice though, and it might be useful for understanding related methods in the future.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Riemannian Optimization for Skip-Gram Negative Sampling
Skip-Gram Negative Sampling (SGNS) word embedding model, well known by its implementation in *word2vec* software, is usually optimized by stochastic gradient descent. It can be shown that optimizing for SGNS objective can be viewed as an optimization problem of searching for a good matrix with the low-rank constraint. The most standard way to solve this type of problems is to apply Riemannian optimization framework to optimize the SGNS objective over the manifold of required low-rank matrices. In this paper, we propose an algorithm that optimizes SGNS objective using Riemannian optimization and demonstrates its superiority over popular competitors, such as the original method to train SGNS and SVD over SPPMI matrix.
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<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper presents a principled optimization method for SGNS (word2vec). While the proposed method is elegant from a theoretical perspective, I am not sure what the tangible benefits of this approach are. For example, does using Riemannian optimization allow the model to converge faster than the alternatives? The evaluation doesn*t show a dramatic advantage to RO-SGNS; the 1% difference on the word similarity benchmarks is within the range of hyperparameter effects (see *Improving Distributional Similarity with Lessons Learned from Word Embeddings*, (Levy et al., 2015)). The theoretical connection to Riemannian optimization is nice though, and it might be useful for understanding related methods in the future.
assistant
This paper presents a principled optimization method for SGNS (word2vec). While the proposed method is elegant from a theoretical perspective, I am not sure what the tangible benefits of this approach are. For example, does using Riemannian optimization allow the model to converge faster than the alternatives? The evaluation doesn*t show a dramatic advantage to RO-SGNS; the 1% difference on the word similarity benchmarks is within the range of hyperparameter effects (see *Improving Distributional Similarity with Lessons Learned from Word Embeddings*, (Levy et al., 2015)). The theoretical connection to Riemannian optimization is nice though, and it might be useful for understanding related methods in the future.