Adversarial Multi-task Learning for Text Classification (1704.05742v1)

Abstract: Neural network models have shown their promising opportunities for multi-task learning, which focus on learning the shared layers to extract the common and task-invariant features. However, in most existing approaches, the extracted shared features are prone to be contaminated by task-specific features or the noise brought by other tasks. In this paper, we propose an adversarial multi-task learning framework, alleviating the shared and private latent feature spaces from interfering with each other. We conduct extensive experiments on 16 different text classification tasks, which demonstrates the benefits of our approach. Besides, we show that the shared knowledge learned by our proposed model can be regarded as off-the-shelf knowledge and easily transferred to new tasks. The datasets of all 16 tasks are publicly available at \url{http://nlp.fudan.edu.cn/data/}

• The paper introduces an adversarial multi-task learning framework that segregates shared and task-specific features using adversarial training and orthogonality constraints.
• The adversarial shared-private model (ASP-MTL) outperforms traditional MTL methods, achieving a 4.1% error rate reduction across 16 text classification tasks.
• The approach offers practical applications by enabling rapid transfer of shared knowledge to new domains like sentiment analysis and domain adaptation.

Adversarial Multi-task Learning for Text Classification: An Expert Review

The paper "Adversarial Multi-task Learning for Text Classification" by Pengfei Liu, Xipeng Qiu, and Xuanjing Huang presents a novel approach to multi-task learning (MTL) in text classification. The framework resolves the interference issues between shared and task-specific features by introducing adversarial training and orthogonality constraints. This essay provides a comprehensive overview of the research methodology, numerical results, and potential implications of the paper.

Methodological Advances

The proposed framework highlights two main strategies: adversarial training and orthogonality constraints. These approaches aim to effectively segregate shared and private feature spaces, thus minimizing feature contamination—a common drawback in existing MTL approaches.

1. Adversarial Training: The framework employs adversarial training to remove task-specific biases from the shared feature space. By extending binary adversarial training to handle multi-class tasks, the model jointly trains multiple tasks, thus improving generalizability and robustness. This is achieved through a min-max optimization where the shared feature extractor is trained to mislead a discriminator tasked with identifying the source of the encoded features.
2. Orthogonality Constraints: To further ensure that shared and private features do not overlap, orthogonality constraints are applied. These constraints foster a disjoint relationship between the two feature spaces, leveraging the shared space for task-invariant features while reserving task-specific information in a separate domain.

Experimental Outcomes

The framework was subjected to extensive experimental validation on 16 diverse text classification tasks. Key findings include:

• The adversarial shared-private model (ASP-MTL) demonstrated superior accuracy across various tasks compared to traditional and other neural network-based MTL frameworks.
• ASP-MTL achieved an average error rate reduction of 4.1% across tasks, indicating the efficacy of the proposed adversarial and orthogonal approach.

Implications and Future Directions

The outcomes of this paper have both theoretical and practical implications. Theoretically, the approach contributes to the understanding of effective knowledge sharing in MTL by clearly distinguishing between shared and private information. Practically, the framework's ability to generate off-the-shelf shared knowledge that can be easily transferred to new tasks suggests potential applications in domains requiring rapid adaptation to new data, such as sentiment analysis and domain adaptation.

Future work could explore refining the adversarial framework to handle more complex and heterogeneous task sets potentially involving different modalities. Additionally, the exploration of unsupervised or semi-supervised settings could further enhance the adaptability of the approach to diverse real-world scenarios.

In summary, the paper presents a significant advancement in the domain of text classification through a robust adversarial multi-task learning framework. The integration of adversarial training and orthogonality constraints addresses critical issues in feature space contamination, offering a valuable tool for improving performance across numerous NLP tasks.