A NON-LINEAR THEORY FOR SENTENCE EMBEDDING
This paper revisits the Random Walk model for sentence embedding in the context of non-extensive statistics. We propose a non-extensive algebra to compute the discourse vector. We argue that by doing so we are taking into account high non-linearity in the semantic space. Furthermore, we show that by considering a non-extensive algebra, the compounding effect of the vector length is mitigated. Overall, we show that the proposed model leads to good sentence embedding. We evaluate the embedding method on textual similarity tasks.
 Summary: this paper discussed an incremental improvement over the Random Walk based model for sentence embedding. Conclusion: this paper is not ready for publication, very poor written and well below the bar of ICLR-caliber papers. More: This paper spent the majority of its content explaining background (those paragraphs were very poor written and difficult to read), and very briefly introduced their methodology with some mathematical derivations and equations, most of which can be put in the supplement instead of main context. The author didn*t quite explain how the proposed method, such as why using non-extensive statistic in this context, The experiment results aren*t convincing and lack sufficient information for reproducibility.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
A NON-LINEAR THEORY FOR SENTENCE EMBEDDING
This paper revisits the Random Walk model for sentence embedding in the context of non-extensive statistics. We propose a non-extensive algebra to compute the discourse vector. We argue that by doing so we are taking into account high non-linearity in the semantic space. Furthermore, we show that by considering a non-extensive algebra, the compounding effect of the vector length is mitigated. Overall, we show that the proposed model leads to good sentence embedding. We evaluate the embedding method on textual similarity tasks.
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>



 Summary: this paper discussed an incremental improvement over the Random Walk based model for sentence embedding. Conclusion: this paper is not ready for publication, very poor written and well below the bar of ICLR-caliber papers. More: This paper spent the majority of its content explaining background (those paragraphs were very poor written and difficult to read), and very briefly introduced their methodology with some mathematical derivations and equations, most of which can be put in the supplement instead of main context. The author didn*t quite explain how the proposed method, such as why using non-extensive statistic in this context, The experiment results aren*t convincing and lack sufficient information for reproducibility.
assistant
 Summary: this paper discussed an incremental improvement over the Random Walk based model for sentence embedding. Conclusion: this paper is not ready for publication, very poor written and well below the bar of ICLR-caliber papers. More: This paper spent the majority of its content explaining background (those paragraphs were very poor written and difficult to read), and very briefly introduced their methodology with some mathematical derivations and equations, most of which can be put in the supplement instead of main context. The author didn*t quite explain how the proposed method, such as why using non-extensive statistic in this context, The experiment results aren*t convincing and lack sufficient information for reproducibility.