Binary Paragraph Vectors
Recently Le & Mikolov described two log-linear models, called Paragraph Vector, that can be used to learn state-of-the-art distributed representations of documents. Inspired by this work, we present Binary Paragraph Vector models: simple neural networks that learn short binary codes for fast information retrieval. We show that binary paragraph vectors outperform autoencoder-based binary codes, despite using fewer bits. We also evaluate their precision in transfer learning settings, where binary codes are inferred for documents unrelated to the training corpus. Results from these experiments indicate that binary paragraph vectors can capture semantics relevant for various domain-specific documents. Finally, we present a model that simultaneously learns short binary codes and longer, real-valued representations. This model can be used to rapidly retrieve a short list of highly relevant documents from a large document collection.
This work proposes a model that can learn short binary codes via paragraph vectors to allow fast retrieval of documents. The experiments show that this is superior to semantic hashing. The approach is simple and not very technically interesting. For a code size of 128, the loss compared to a continuous paragraph vector seems moderate. The paper asks the reader to refer to the Salakhutdinov and Hinton paper for the baseline numbers but I think they should be placed in the paper for easy reference. For simplicity, the paper could show the precision at 12.5%, 25% and 50% recall for the proposed model and semantic hashing. It also seems that the semantic hashing paper shows results on RCV2 and not RCV1. RCV1 is twice the size of RCV2 and is English only so it seems that these results are not comparable. It would be interesting to see how many binary bits are required to match the performance of the continuous representation. A comparison to the continuous PV-DBOW trained with bigrams would also make it a more fair comparison. Figure 7 in the paper shows a loss from using the real-binary PV-DBOW. It seems that if a user needed high quality ranking after the retrieval stage and they could afford the extra space and computation, then it would be better for them to use a standard PV-DBOW to obtain the continuous representation at that stage. Minor comments: First line after the introduction: is sheer -> is the sheer 4th line from the bottom of P1: words embeddings -> word embeddings In table 1: What does code size refer to for PV-DBOW? Is this the number of elements in the continuous vector? 5th line from the bottom of P5: W -> We 5th line after section 3.1: covers wide -> covers a wide
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Binary Paragraph Vectors
Recently Le & Mikolov described two log-linear models, called Paragraph Vector, that can be used to learn state-of-the-art distributed representations of documents. Inspired by this work, we present Binary Paragraph Vector models: simple neural networks that learn short binary codes for fast information retrieval. We show that binary paragraph vectors outperform autoencoder-based binary codes, despite using fewer bits. We also evaluate their precision in transfer learning settings, where binary codes are inferred for documents unrelated to the training corpus. Results from these experiments indicate that binary paragraph vectors can capture semantics relevant for various domain-specific documents. Finally, we present a model that simultaneously learns short binary codes and longer, real-valued representations. This model can be used to rapidly retrieve a short list of highly relevant documents from a large document collection.
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 work proposes a model that can learn short binary codes via paragraph vectors to allow fast retrieval of documents. The experiments show that this is superior to semantic hashing. The approach is simple and not very technically interesting. For a code size of 128, the loss compared to a continuous paragraph vector seems moderate. The paper asks the reader to refer to the Salakhutdinov and Hinton paper for the baseline numbers but I think they should be placed in the paper for easy reference. For simplicity, the paper could show the precision at 12.5%, 25% and 50% recall for the proposed model and semantic hashing. It also seems that the semantic hashing paper shows results on RCV2 and not RCV1. RCV1 is twice the size of RCV2 and is English only so it seems that these results are not comparable. It would be interesting to see how many binary bits are required to match the performance of the continuous representation. A comparison to the continuous PV-DBOW trained with bigrams would also make it a more fair comparison. Figure 7 in the paper shows a loss from using the real-binary PV-DBOW. It seems that if a user needed high quality ranking after the retrieval stage and they could afford the extra space and computation, then it would be better for them to use a standard PV-DBOW to obtain the continuous representation at that stage. Minor comments: First line after the introduction: is sheer -> is the sheer 4th line from the bottom of P1: words embeddings -> word embeddings In table 1: What does code size refer to for PV-DBOW? Is this the number of elements in the continuous vector? 5th line from the bottom of P5: W -> We 5th line after section 3.1: covers wide -> covers a wide
assistant
This work proposes a model that can learn short binary codes via paragraph vectors to allow fast retrieval of documents. The experiments show that this is superior to semantic hashing. The approach is simple and not very technically interesting. For a code size of 128, the loss compared to a continuous paragraph vector seems moderate. The paper asks the reader to refer to the Salakhutdinov and Hinton paper for the baseline numbers but I think they should be placed in the paper for easy reference. For simplicity, the paper could show the precision at 12.5%, 25% and 50% recall for the proposed model and semantic hashing. It also seems that the semantic hashing paper shows results on RCV2 and not RCV1. RCV1 is twice the size of RCV2 and is English only so it seems that these results are not comparable. It would be interesting to see how many binary bits are required to match the performance of the continuous representation. A comparison to the continuous PV-DBOW trained with bigrams would also make it a more fair comparison. Figure 7 in the paper shows a loss from using the real-binary PV-DBOW. It seems that if a user needed high quality ranking after the retrieval stage and they could afford the extra space and computation, then it would be better for them to use a standard PV-DBOW to obtain the continuous representation at that stage. Minor comments: First line after the introduction: is sheer -> is the sheer 4th line from the bottom of P1: words embeddings -> word embeddings In table 1: What does code size refer to for PV-DBOW? Is this the number of elements in the continuous vector? 5th line from the bottom of P5: W -> We 5th line after section 3.1: covers wide -> covers a wide