Investigating Capsule Networks with Dynamic Routing for Text Classification (1804.00538v4)

Abstract: In this study, we explore capsule networks with dynamic routing for text classification. We propose three strategies to stabilize the dynamic routing process to alleviate the disturbance of some noise capsules which may contain "background" information or have not been successfully trained. A series of experiments are conducted with capsule networks on six text classification benchmarks. Capsule networks achieve state of the art on 4 out of 6 datasets, which shows the effectiveness of capsule networks for text classification. We additionally show that capsule networks exhibit significant improvement when transfer single-label to multi-label text classification over strong baseline methods. To the best of our knowledge, this is the first work that capsule networks have been empirically investigated for text modeling.

• The paper introduces three stabilization strategies—Orphan Category, Leaky-Softmax, and Coefficients Amendment—to mitigate background noise in dynamic routing.
• It employs comprehensive evaluations on six standard datasets, demonstrating competitive performance over traditional CNN and LSTM models.
• The findings indicate that capsule networks effectively capture hierarchical text features, excelling in both single-label and multi-label classification tasks.

Investigating Capsule Networks with Dynamic Routing for Text Classification

The paper explores the utilization of capsule networks with dynamic routing strategies for enhancing text classification tasks. This research introduces and systematically investigates three stabilization strategies to counteract the destabilizing effects posed by noisy capsules, which can emerge due to irrelevant or poorly trained components within the network—often referred to as "background" noise. The usefulness of these strategies is evaluated across six established text classification benchmarks, where capsule networks perform favorably against baseline methodologies.

Key Propositions and Methodologies

The paper is anchored on the foundational concept of capsule networks that were initially formulated to overcome the limitations of traditional CNNs in recognizing spatial hierarchies and relationships. Capsules group neurons to capture instantiation parameters, thereby potentially preserving rich structural information about the input data. In this context, the paper proposes:

1. Orphan Category: Introducing an extra "orphan" category within the capsule network to effectively route background noise present in text data such as common stop-words, thereby enhancing semantic learning relevant to the classification task.
2. Leaky-Softmax: A novel adaptation within the routing algorithm using leaky-softmax to mitigate overconfidence from dynamic routing, which can otherwise aggravate inaccuracies due to noise.
3. Coefficients Amendment: Utilizing the probability encapsulation of child capsules to iteratively refine the connection strengths, facilitating a more stable routing process.

Experimental Evaluation

The empirical evaluation involves a comprehensive analysis on six standard datasets for text classification, including MR, SST-2, Subj, TREC, CR, and AG's news corpus. The quantitative results reveal that capsule networks, particularly with the deployment of these proposed strategies, achieve competitive performance metrics. Notably, Capsule-B stands out, showcasing consistent and superior accuracy due to its ability to leverage a parallel architecture that integrates multiple n-gram convolutional representations.

The paper also rigorously explores the transferability of capsule networks from single-label to multi-label classification tasks. By examining the Reuters-21578 dataset, a significant improvement over traditional LSTM and CNN-based models is demonstrated. This highlights the capability of capsule networks in modeling label hierarchies, thereby generalizing effectively in multi-label contexts.

Implications and Future Directions

The implications of this research are multifaceted, providing both practical and theoretical advancements:

• Practical Implications: The implementation of capsule networks in text classification presents an advance in how text data, rich in sequence and hierarchical dependencies, can be modeled. The improvements in transfer learning indicate a promising trajectory for applying capsule networks to complex real-world tasks involving multi-label classification.
• Theoretical Implications: The stabilization strategies introduced bridge a critical gap in capsule network training, addressing prior limitations associated with instability and noise, thereby setting a new direction in dynamic routing research.

Moving forward, potential developments could explore further refinement of capsule architectures to reduce computational overhead or enhance interpretability regarding which features capsules deem pertinent. Moreover, integrating advanced linguistic pre-processing or transformer-based embeddings prior to capsule processing might synergistically enhance performance across more diverse NLP tasks. As the community continues to refine capsule-based methodologies, their integration into NLP could herald a shift towards more nuanced and semantically robust LLMs.