Skip-gram word embeddings in hyperbolic space
Embeddings of tree-like graphs in hyperbolic space were recently shown to surpass their Euclidean counterparts in performance by a large margin. Inspired by these results, we present an algorithm for learning word embeddings in hyperbolic space from free text. An objective function based on the hyperbolic distance is derived and included in the skip-gram negative-sampling architecture from word2vec. The hyperbolic word embeddings are then evaluated on word similarity and analogy benchmarks. The results demonstrate the potential of hyperbolic word embeddings, particularly in low dimensions, though without clear superiority over their Euclidean counterparts. We further discuss subtleties in the formulation of the analogy task in curved spaces.
 The present paper aims to apply recent developments in hyperbolic embeddings of graphs to the area of word embeddings. The paper is relatively clearly written and looks technically correct. The main contribution of the paper is in suggesting the usage of Minkowski dot-product instead of Eucledian dot-product in skip-gram model and derivation of corresponding weight update formulas taking into account the peculiarities of hyperbolic space. The suggested aproach is realtively simple, though requiring adding additional bias parameter, which doesn*t look entirely natural for the problem considered. I should note, that all the update equations are relatively straitforward given the results of recent papers on hyperbolic embeddings for graphs. Experimental results show some mild to no improvement over classical skip gram model in word similarity and word analogy problems. My main concern about the paper is that it is not entirely clear throughout the text why the proposed model should be better than any of the baselines. Currently it lookss like the paper merging 2 ideas without any clear expalanation why they should work well together. I believe that the proposed approach (or similar one) might be useful for practice of natural language processing, but to asses that one would need to base on clear motivation and support this motivation with some examples showing that hyperbolicity indeed helps to capture semantics better (like famous world analogy examples for word2vec). Pros: - clearly written - technically correct Cons: - technically straightforward - not convincing experiments - unclear, why the approach should work
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Skip-gram word embeddings in hyperbolic space
Embeddings of tree-like graphs in hyperbolic space were recently shown to surpass their Euclidean counterparts in performance by a large margin. Inspired by these results, we present an algorithm for learning word embeddings in hyperbolic space from free text. An objective function based on the hyperbolic distance is derived and included in the skip-gram negative-sampling architecture from word2vec. The hyperbolic word embeddings are then evaluated on word similarity and analogy benchmarks. The results demonstrate the potential of hyperbolic word embeddings, particularly in low dimensions, though without clear superiority over their Euclidean counterparts. We further discuss subtleties in the formulation of the analogy task in curved spaces.
user
<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>



 The present paper aims to apply recent developments in hyperbolic embeddings of graphs to the area of word embeddings. The paper is relatively clearly written and looks technically correct. The main contribution of the paper is in suggesting the usage of Minkowski dot-product instead of Eucledian dot-product in skip-gram model and derivation of corresponding weight update formulas taking into account the peculiarities of hyperbolic space. The suggested aproach is realtively simple, though requiring adding additional bias parameter, which doesn*t look entirely natural for the problem considered. I should note, that all the update equations are relatively straitforward given the results of recent papers on hyperbolic embeddings for graphs. Experimental results show some mild to no improvement over classical skip gram model in word similarity and word analogy problems. My main concern about the paper is that it is not entirely clear throughout the text why the proposed model should be better than any of the baselines. Currently it lookss like the paper merging 2 ideas without any clear expalanation why they should work well together. I believe that the proposed approach (or similar one) might be useful for practice of natural language processing, but to asses that one would need to base on clear motivation and support this motivation with some examples showing that hyperbolicity indeed helps to capture semantics better (like famous world analogy examples for word2vec). Pros: - clearly written - technically correct Cons: - technically straightforward - not convincing experiments - unclear, why the approach should work
assistant
 The present paper aims to apply recent developments in hyperbolic embeddings of graphs to the area of word embeddings. The paper is relatively clearly written and looks technically correct. The main contribution of the paper is in suggesting the usage of Minkowski dot-product instead of Eucledian dot-product in skip-gram model and derivation of corresponding weight update formulas taking into account the peculiarities of hyperbolic space. The suggested aproach is realtively simple, though requiring adding additional bias parameter, which doesn*t look entirely natural for the problem considered. I should note, that all the update equations are relatively straitforward given the results of recent papers on hyperbolic embeddings for graphs. Experimental results show some mild to no improvement over classical skip gram model in word similarity and word analogy problems. My main concern about the paper is that it is not entirely clear throughout the text why the proposed model should be better than any of the baselines. Currently it lookss like the paper merging 2 ideas without any clear expalanation why they should work well together. I believe that the proposed approach (or similar one) might be useful for practice of natural language processing, but to asses that one would need to base on clear motivation and support this motivation with some examples showing that hyperbolicity indeed helps to capture semantics better (like famous world analogy examples for word2vec). Pros: - clearly written - technically correct Cons: - technically straightforward - not convincing experiments - unclear, why the approach should work