Efficient Vector Representation for Documents through Corruption
We present an efficient document representation learning framework, Document Vector through Corruption (Doc2VecC). Doc2VecC represents each document as a simple average of word embeddings. It ensures a representation generated as such captures the semantic meanings of the document during learning. A corruption model is included, which introduces a data-dependent regularization that favors informative or rare words while forcing the embeddings of common and non-discriminative ones to be close to zero. Doc2VecC produces significantly better word embeddings than Word2Vec. We compare Doc2VecC with several state-of-the-art document representation learning algorithms. The simple model architecture introduced by Doc2VecC matches or out-performs the state-of-the-art in generating high-quality document representations for sentiment analysis, document classification as well as semantic relatedness tasks. The simplicity of the model enables training on billions of words per hour on a single machine. At the same time, the model is very efficient in generating representations of unseen documents at test time.
This paper discusses a method for computing vector representations for documents by using a skip-gram style learning mechanism with an added regularizer in the form of a global context vector with various bits of drop out. While none of the individual components proposed in this paper are new, I believe that the combination in this fashion is. Further, I appreciated the detailed analysis of model behaviour in section 3. The main downside to this submission is in its relative weakness on the empirical front. Arguably there are more interesting tasks than sentiment analysis and k-way classification! Likewise, why waste 2/3 of a page on t-sne projections rather than use that space for further analysis? While I am a bit disappointed by this reduced evaluation and agree with the other reviewers concerning soft baselines, I think this paper should be accepted: it*s an interesting algorithm, nicely composed and very efficient, so it*s reasonable to assume that other readers might have use for some of the ideas presented here.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Efficient Vector Representation for Documents through Corruption
We present an efficient document representation learning framework, Document Vector through Corruption (Doc2VecC). Doc2VecC represents each document as a simple average of word embeddings. It ensures a representation generated as such captures the semantic meanings of the document during learning. A corruption model is included, which introduces a data-dependent regularization that favors informative or rare words while forcing the embeddings of common and non-discriminative ones to be close to zero. Doc2VecC produces significantly better word embeddings than Word2Vec. We compare Doc2VecC with several state-of-the-art document representation learning algorithms. The simple model architecture introduced by Doc2VecC matches or out-performs the state-of-the-art in generating high-quality document representations for sentiment analysis, document classification as well as semantic relatedness tasks. The simplicity of the model enables training on billions of words per hour on a single machine. At the same time, the model is very efficient in generating representations of unseen documents at test time.
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>



This paper discusses a method for computing vector representations for documents by using a skip-gram style learning mechanism with an added regularizer in the form of a global context vector with various bits of drop out. While none of the individual components proposed in this paper are new, I believe that the combination in this fashion is. Further, I appreciated the detailed analysis of model behaviour in section 3. The main downside to this submission is in its relative weakness on the empirical front. Arguably there are more interesting tasks than sentiment analysis and k-way classification! Likewise, why waste 2/3 of a page on t-sne projections rather than use that space for further analysis? While I am a bit disappointed by this reduced evaluation and agree with the other reviewers concerning soft baselines, I think this paper should be accepted: it*s an interesting algorithm, nicely composed and very efficient, so it*s reasonable to assume that other readers might have use for some of the ideas presented here.
assistant
This paper discusses a method for computing vector representations for documents by using a skip-gram style learning mechanism with an added regularizer in the form of a global context vector with various bits of drop out. While none of the individual components proposed in this paper are new, I believe that the combination in this fashion is. Further, I appreciated the detailed analysis of model behaviour in section 3. The main downside to this submission is in its relative weakness on the empirical front. Arguably there are more interesting tasks than sentiment analysis and k-way classification! Likewise, why waste 2/3 of a page on t-sne projections rather than use that space for further analysis? While I am a bit disappointed by this reduced evaluation and agree with the other reviewers concerning soft baselines, I think this paper should be accepted: it*s an interesting algorithm, nicely composed and very efficient, so it*s reasonable to assume that other readers might have use for some of the ideas presented here.