Generative Paragraph Vector
The recently introduced Paragraph Vector is an efficient method for learning high-quality distributed representations for pieces of texts. However, an inherent limitation of Paragraph Vector is lack of ability to infer distributed representations for texts outside of the training set. To tackle this problem, we introduce a Generative Paragraph Vector, which can be viewed as a probabilistic extension of the Distributed Bag of Words version of Paragraph Vector with a complete generative process. With the ability to infer the distributed representations for unseen texts, we can further incorporate text labels into the model and turn it into a supervised version, namely Supervised Generative Paragraph Vector. In this way, we can leverage the labels paired with the texts to guide the representation learning, and employ the learned model for prediction tasks directly. Experiments on five text classification benchmark collections show that both model architectures can yield superior classification performance over the state-of-the-art counterparts.
While this paper has some decent accuracy numbers, it is hard to argue for acceptance given the following: 1) motivation based on the incorrect assumption that the Paragraph Vector wouldn*t work on unseen data 2) Numerous basic formatting and Bibtex citation issues. Lack of novelty of yet another standard directed LDA-like bag of words/bigram model.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Generative Paragraph Vector
The recently introduced Paragraph Vector is an efficient method for learning high-quality distributed representations for pieces of texts. However, an inherent limitation of Paragraph Vector is lack of ability to infer distributed representations for texts outside of the training set. To tackle this problem, we introduce a Generative Paragraph Vector, which can be viewed as a probabilistic extension of the Distributed Bag of Words version of Paragraph Vector with a complete generative process. With the ability to infer the distributed representations for unseen texts, we can further incorporate text labels into the model and turn it into a supervised version, namely Supervised Generative Paragraph Vector. In this way, we can leverage the labels paired with the texts to guide the representation learning, and employ the learned model for prediction tasks directly. Experiments on five text classification benchmark collections show that both model architectures can yield superior classification performance over the state-of-the-art counterparts.
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>



While this paper has some decent accuracy numbers, it is hard to argue for acceptance given the following: 1) motivation based on the incorrect assumption that the Paragraph Vector wouldn*t work on unseen data 2) Numerous basic formatting and Bibtex citation issues. Lack of novelty of yet another standard directed LDA-like bag of words/bigram model.
assistant
While this paper has some decent accuracy numbers, it is hard to argue for acceptance given the following: 1) motivation based on the incorrect assumption that the Paragraph Vector wouldn*t work on unseen data 2) Numerous basic formatting and Bibtex citation issues. Lack of novelty of yet another standard directed LDA-like bag of words/bigram model.