Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic Formulae
Embeddings are a fundamental component of many modern machine learning and natural language processing models. Understanding them and visualizing them is essential for gathering insights about the information they capture and the behavior of the models. State of the art in analyzing embeddings consists in projecting them in two-dimensional planes without any interpretable semantics associated to the axes of the projection, which makes detailed analyses and comparison among multiple sets of embeddings challenging. In this work, we propose to use explicit axes defined as algebraic formulae over embeddings to project them into a lower dimensional, but semantically meaningful subspace, as a simple yet effective analysis and visualization methodology. This methodology assigns an interpretable semantics to the measures of variability and the axes of visualizations, allowing for both comparisons among different sets of embeddings and fine-grained inspection of the embedding spaces. We demonstrate the power of the proposed methodology through a series of case studies that make use of visualizations constructed around the underlying methodology and through a user study. The results show how the methodology is effective at providing more profound insights than classical projection methods and how it is widely applicable to many other use cases.
 To the best of my understanding the paper proposes some methodological ideas for visualizing and analyzing representations. The paper is unclear mainly because it is a bit difficult to pinpoint the contribution and its audience. What would help me better understand and potentially raise my rating is an analysis of a classical model on a known dataset as a case study and some interesting findings would help make it more exciting and give the readers more incentives to try this out. Like train an AlexNet and VGG imagenet model and show that the embeddings are better aligned with the wordnet taxonomy in one of the other. This should be possible with their approach if i understand it correctly. pros: - visualization and analysis is a very exciting and important topic in machine learning - this is clearly useful if it worked cons: - not sure what the contribution claim for the paper is since these types of plots existed already in the literature (is it a popularization claim ?)
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic Formulae
Embeddings are a fundamental component of many modern machine learning and natural language processing models. Understanding them and visualizing them is essential for gathering insights about the information they capture and the behavior of the models. State of the art in analyzing embeddings consists in projecting them in two-dimensional planes without any interpretable semantics associated to the axes of the projection, which makes detailed analyses and comparison among multiple sets of embeddings challenging. In this work, we propose to use explicit axes defined as algebraic formulae over embeddings to project them into a lower dimensional, but semantically meaningful subspace, as a simple yet effective analysis and visualization methodology. This methodology assigns an interpretable semantics to the measures of variability and the axes of visualizations, allowing for both comparisons among different sets of embeddings and fine-grained inspection of the embedding spaces. We demonstrate the power of the proposed methodology through a series of case studies that make use of visualizations constructed around the underlying methodology and through a user study. The results show how the methodology is effective at providing more profound insights than classical projection methods and how it is widely applicable to many other use cases.
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>



 To the best of my understanding the paper proposes some methodological ideas for visualizing and analyzing representations. The paper is unclear mainly because it is a bit difficult to pinpoint the contribution and its audience. What would help me better understand and potentially raise my rating is an analysis of a classical model on a known dataset as a case study and some interesting findings would help make it more exciting and give the readers more incentives to try this out. Like train an AlexNet and VGG imagenet model and show that the embeddings are better aligned with the wordnet taxonomy in one of the other. This should be possible with their approach if i understand it correctly. pros: - visualization and analysis is a very exciting and important topic in machine learning - this is clearly useful if it worked cons: - not sure what the contribution claim for the paper is since these types of plots existed already in the literature (is it a popularization claim ?)
assistant
 To the best of my understanding the paper proposes some methodological ideas for visualizing and analyzing representations. The paper is unclear mainly because it is a bit difficult to pinpoint the contribution and its audience. What would help me better understand and potentially raise my rating is an analysis of a classical model on a known dataset as a case study and some interesting findings would help make it more exciting and give the readers more incentives to try this out. Like train an AlexNet and VGG imagenet model and show that the embeddings are better aligned with the wordnet taxonomy in one of the other. This should be possible with their approach if i understand it correctly. pros: - visualization and analysis is a very exciting and important topic in machine learning - this is clearly useful if it worked cons: - not sure what the contribution claim for the paper is since these types of plots existed already in the literature (is it a popularization claim ?)