TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency
In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based language model designed to directly capture the global semantic meaning relating words in a document via latent topics. Because of their sequential nature, RNNs are good at capturing the local structure of a word sequence – both semantic and syntactic – but might face difficulty remembering long-range dependencies. Intuitively, these long-range dependencies are of semantic nature. In contrast, latent topic models are able to capture the global underlying semantic structure of a document but do not account for word ordering. The proposed TopicRNN model integrates the merits of RNNs and latent topic models: it captures local (syntactic) dependencies using an RNN and global (semantic) dependencies using latent topics. Unlike previous work on contextual RNN language modeling, our model is learned end-to-end. Empirical results on word prediction show that TopicRNN outperforms existing contextual RNN baselines. In addition, TopicRNN can be used as an unsupervised feature extractor for documents. We do this for sentiment analysis on the IMDB movie review dataset and report an error rate of 6.28%. This is comparable to the state-of-the-art 5.91% resulting from a semi-supervised approach. Finally, TopicRNN also yields sensible topics, making it a useful alternative to document models such as latent Dirichlet allocation.
This paper introduces a model that blends ideas from generative topic models with those from recurrent neural network language models. The authors evaluate the proposed approach on a document level classification benchmark as well as a language modeling benchmark and it seems to work well. There is also some analysis as to topics learned by the model and its ability to generate text. Overall the paper is clearly written and with the code promised by the authors others should be able to re-implement the approach. I have 2 potentially major questions I would ask the authors to address: 1 - LDA topic models make an exchangability (bag of words) assumption. The discussion of the generative story for TopicRNN should explicitly discuss whether this assumption is also made. On the surface it appears it is since y_t is sampled using only the document topic vector and h_t but we know that in practice h_t comes from a recurrent model that observes y_t-1. Not clear how this clean exposition of the generative model relates to what is actually done. In the Generating sequential text section it’s clear the topic model can’t generate words without using y_1 - t-1 but this seems inconsistent with the generative model specification. This needs to be shown in the paper and made clear to have a complete paper. 2 - The topic model only allows for linear interactions of the topic vector theta. It seems like this might be required to keep the generative model tractable but seems like a very poor assumption. We would expect the topic representation to have rich interactions with a language model to create nonlinear adjustments to word probabilities for a document. Please add discussion as to why this modeling choice exists and if possible how future work could modify that assumption (or explain why it’s not such a bad assumption as one might imagine) Figure 2 colors very difficult to distinguish.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency
In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based language model designed to directly capture the global semantic meaning relating words in a document via latent topics. Because of their sequential nature, RNNs are good at capturing the local structure of a word sequence – both semantic and syntactic – but might face difficulty remembering long-range dependencies. Intuitively, these long-range dependencies are of semantic nature. In contrast, latent topic models are able to capture the global underlying semantic structure of a document but do not account for word ordering. The proposed TopicRNN model integrates the merits of RNNs and latent topic models: it captures local (syntactic) dependencies using an RNN and global (semantic) dependencies using latent topics. Unlike previous work on contextual RNN language modeling, our model is learned end-to-end. Empirical results on word prediction show that TopicRNN outperforms existing contextual RNN baselines. In addition, TopicRNN can be used as an unsupervised feature extractor for documents. We do this for sentiment analysis on the IMDB movie review dataset and report an error rate of 6.28%. This is comparable to the state-of-the-art 5.91% resulting from a semi-supervised approach. Finally, TopicRNN also yields sensible topics, making it a useful alternative to document models such as latent Dirichlet allocation.
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 introduces a model that blends ideas from generative topic models with those from recurrent neural network language models. The authors evaluate the proposed approach on a document level classification benchmark as well as a language modeling benchmark and it seems to work well. There is also some analysis as to topics learned by the model and its ability to generate text. Overall the paper is clearly written and with the code promised by the authors others should be able to re-implement the approach. I have 2 potentially major questions I would ask the authors to address: 1 - LDA topic models make an exchangability (bag of words) assumption. The discussion of the generative story for TopicRNN should explicitly discuss whether this assumption is also made. On the surface it appears it is since y_t is sampled using only the document topic vector and h_t but we know that in practice h_t comes from a recurrent model that observes y_t-1. Not clear how this clean exposition of the generative model relates to what is actually done. In the Generating sequential text section it’s clear the topic model can’t generate words without using y_1 - t-1 but this seems inconsistent with the generative model specification. This needs to be shown in the paper and made clear to have a complete paper. 2 - The topic model only allows for linear interactions of the topic vector theta. It seems like this might be required to keep the generative model tractable but seems like a very poor assumption. We would expect the topic representation to have rich interactions with a language model to create nonlinear adjustments to word probabilities for a document. Please add discussion as to why this modeling choice exists and if possible how future work could modify that assumption (or explain why it’s not such a bad assumption as one might imagine) Figure 2 colors very difficult to distinguish.
assistant
This paper introduces a model that blends ideas from generative topic models with those from recurrent neural network language models. The authors evaluate the proposed approach on a document level classification benchmark as well as a language modeling benchmark and it seems to work well. There is also some analysis as to topics learned by the model and its ability to generate text. Overall the paper is clearly written and with the code promised by the authors others should be able to re-implement the approach. I have 2 potentially major questions I would ask the authors to address: 1 - LDA topic models make an exchangability (bag of words) assumption. The discussion of the generative story for TopicRNN should explicitly discuss whether this assumption is also made. On the surface it appears it is since y_t is sampled using only the document topic vector and h_t but we know that in practice h_t comes from a recurrent model that observes y_t-1. Not clear how this clean exposition of the generative model relates to what is actually done. In the Generating sequential text section it’s clear the topic model can’t generate words without using y_1 - t-1 but this seems inconsistent with the generative model specification. This needs to be shown in the paper and made clear to have a complete paper. 2 - The topic model only allows for linear interactions of the topic vector theta. It seems like this might be required to keep the generative model tractable but seems like a very poor assumption. We would expect the topic representation to have rich interactions with a language model to create nonlinear adjustments to word probabilities for a document. Please add discussion as to why this modeling choice exists and if possible how future work could modify that assumption (or explain why it’s not such a bad assumption as one might imagine) Figure 2 colors very difficult to distinguish.