TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency (1611.01702v2)

Abstract: In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based LLM 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 LLMing, 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.

Overview of TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency

The paper introduces TopicRNN, an innovative approach to LLMing that integrates the capabilities of Recurrent Neural Networks (RNNs) and latent topic models to address long-range semantic dependencies within text. While RNNs are traditionally effective for modeling local syntactic dependencies, they often struggle with capturing broader semantic context across extensive passages. TopicRNN leverages the strengths of both RNNs and latent topic models to enhance LLMing performance, particularly for tasks requiring the understanding of long-range dependencies.

Core Motivation and Contribution

The primary motivation for constructing TopicRNN stems from the observation that, although RNN-based LLMs have demonstrated substantial successes in capturing the local semantic and syntactic dependencies, they underperform in modeling long-range semantic coherence due to their reliance on sequential memory. Conversely, latent topic models like Latent Dirichlet Allocation (LDA) excel at extracting global semantic structures but fall short in preserving word order, making them inadequate for many LLMing applications needing syntactic understanding.

TopicRNN bridges this gap through an end-to-end trainable framework that deploys an RNN to manage local dependencies while utilizing latent topics to handle semantic context spanning across a document. Notably, this integration circumvents the need for pre-trained topic features, unlike previous hybrid models.

Empirical Results

Empirical evaluations demonstrate that TopicRNN offers superior performance compared to standard contextual RNNs and n-gram models. When tested on the Penn TreeBank dataset for word prediction, TopicRNN achieved lower perplexity scores, signifying enhanced predictive performance, even with a relatively small model size. For instance, TopicGRU, one variant of TopicRNN with 100 neurons, recorded a test perplexity of 112.4, outperforming two stacked LSTMs with 200 neurons each, which marked a perplexity score of 115.9.

Furthermore, TopicRNN also functions effectively as an unsupervised feature extractor in sentiment analysis tasks. On the IMDB movie review dataset, TopicRNN produced document features that yielded an error rate of 6.28%. While marginally higher than the state-of-the-art 5.91% error rate achieved via a more complex semi-supervised adversarial approach, this result highlights TopicRNN’s competitive performance using simpler modeling.

Theoretical and Practical Implications

The theoretical contribution of TopicRNN lies in successfully reinforcing RNN LLMs with global contextual awareness, potentially reshaping how complex language dynamics involving syntax and semantics can be jointly modeled. Practically, TopicRNN extends its utility beyond LLMing to applications like document classification, sentiment analysis, and likely other areas where semantic coherence is crucial.

Future Research Directions

The authors propose several avenues for extending the applicability and efficiency of TopicRNN. One possibility is the dynamic identification and handling of stop words during model training, thereby refining the separation of local and global language phenomena. Also, extending TopicRNN to dialog systems and other context-reliant applications could validate and enhance its versatility and robustness.

In conclusion, TopicRNN represents a proficient advancement in hybrid models, adeptly handling the dual challenges of capturing syntactic subtleties along with semantic depth. This work not only broadens the potential of LLMs in both academic research and deployed systems, but it also lays promising groundwork for future exploration into hybrid neural architectures for language processing tasks.