Efficient Character-level Document Classification by Combining Convolution and Recurrent Layers (1602.00367v1)

Abstract: Document classification tasks were primarily tackled at word level. Recent research that works with character-level inputs shows several benefits over word-level approaches such as natural incorporation of morphemes and better handling of rare words. We propose a neural network architecture that utilizes both convolution and recurrent layers to efficiently encode character inputs. We validate the proposed model on eight large scale document classification tasks and compare with character-level convolution-only models. It achieves comparable performances with much less parameters.

• The paper introduces a novel CRNN architecture that combines convolutional layers for local feature extraction with bidirectional LSTM units to capture long-term dependencies.
• It demonstrates that the hybrid model outperforms traditional convolution-only approaches, achieving lower test error rates with fewer parameters on diverse large-scale datasets.
• The approach offers practical benefits for efficiently handling morphologically rich languages and paves the way for applications in various sequence modeling tasks.

Efficient Character-level Document Classification by Combining Convolution and Recurrent Layers

The paper presents a novel neural network architecture that combines convolutional and recurrent layers for efficient character-level document classification. While traditional methods primarily focus on word-level input representation, this paper leverages character-level inputs, highlighting key advantages such as improved handling of morphologically rich languages and better generalization to rare or unknown words.

Model Architecture and Methodology

The proposed model, referred to as the convolution-recurrent (CRNN) network, processes input text sequences of characters through an embedding layer, followed by convolutional layers and a recurrent layer. The convolutional layers extract local features, while the recurrent layer, implemented with bidirectional LSTM units, captures long-term dependencies within the text. This approach addresses the limitations of convolution-only models, which require many layers to understand long-term dependencies due to their localized receptive fields.

The paper elaborates on the motivation for combining these two types of layers: while convolutional layers are efficient for parallel computation and capturing translation-invariant features, recurrent layers add the ability to model dependencies across the input sequence by retaining contextual information from both the past and future states. The hybrid design leads to a more compact model with a potentially lower number of parameters and computational requirements.

Empirical Evaluation

The CRNN model's performance is evaluated across eight extensive document classification datasets, including tasks like sentiment analysis, ontology classification, and news categorization. The datasets bear diverse scales, ranging from 200,000 to several million documents, and involve multiple classification labels.

Experimental results underscore that the CRNN achieves comparable, and in some cases superior, performance to state-of-the-art convolution-only models on character-level inputs but with significantly fewer parameters. For instance, the CRNN model attains lower test error rates on datasets such as AG's news, Sogou news, DBPedia, and Yahoo! Answers compared to previous deep convolutional models.

Analysis and Implications

The paper explores how architectural choices impact classification outcomes. It notes that fewer convolutional layers, often two or three, suffice for optimal performance, which suggests there is an ideal level of locality to maintain before feeding into the recurrent layer. Moreover, results reveal that the model excels in scenarios with a large number of classes and is less prone to overfitting with smaller training sets due to its fewer parameters.

This work holds several implications for future AI research and practical applications. The proposed hybrid architecture offers a flexible framework that could extend beyond document classification, applicable to tasks in speech recognition, machine translation, or other domains where sequence modeling is essential. Additionally, the approach of substituting recurrent units in place of max-pooling operations presents a promising avenue for preserving detail and context in sequence processing.

Conclusion

Overall, this paper's contributions lie in its efficient integration of convolutional and recurrent units for character-level document representation, offering an attractive solution to some of the limitations encountered with existing models. It demonstrates that capturing both local and global textual dependencies can be achieved with a lighter and potentially more computationally efficient model. Future research may explore the adaptability of this architecture to other modalities and languages, thereby broadening the scope and applicability of this approach in natural language processing and beyond.