A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks (1811.06031v2)

Abstract: Much effort has been devoted to evaluate whether multi-task learning can be leveraged to learn rich representations that can be used in various NLP down-stream applications. However, there is still a lack of understanding of the settings in which multi-task learning has a significant effect. In this work, we introduce a hierarchical model trained in a multi-task learning setup on a set of carefully selected semantic tasks. The model is trained in a hierarchical fashion to introduce an inductive bias by supervising a set of low level tasks at the bottom layers of the model and more complex tasks at the top layers of the model. This model achieves state-of-the-art results on a number of tasks, namely Named Entity Recognition, Entity Mention Detection and Relation Extraction without hand-engineered features or external NLP tools like syntactic parsers. The hierarchical training supervision induces a set of shared semantic representations at lower layers of the model. We show that as we move from the bottom to the top layers of the model, the hidden states of the layers tend to represent more complex semantic information.

• The paper proposes a hierarchical multi-task framework that co-trains four semantic tasks (NER, EMD, RE, CR) to enhance embedding quality.
• It strategically employs bi-directional LSTMs and shortcut connections to integrate linguistic features without relying on external tools.
• Experimental results demonstrate superior task performance and faster convergence through an effective proportional sampling strategy.

A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks

The paper authored by Victor Sanh, Thomas Wolf, and Sebastian Ruder presents a sophisticated approach to training models for NLP through a hierarchical multi-task learning framework. This work targets a nuanced understanding and application of multi-task learning (MTL) to enhance the learning of embeddings from semantic tasks. By selectively assembling four intertwined semantic tasks—namely, Named Entity Recognition (NER), Entity Mention Detection (EMD), Relation Extraction (RE), and Coreference Resolution (CR)—the paper attempts to utilize their interdependencies to improve the generalization and performance of learned models. Notably, the model delivers state-of-the-art results in several of these tasks without reliance on hand-engineered features or external linguistic tools.

Hierarchical Multi-task Learning Framework

This hierarchical framework builds upon the notion that linguistic tasks can be stratified by complexity. The proposed architecture supervises low-level tasks at the model's lower layers while delegating more complex tasks to upper layers. This stratification is designed to introduce an inductive bias, facilitating the learning of representations that naturally evolve from simple to complex semantics as depth increases in the model architecture. The use of shortcut connections across model layers ensures that higher layers can access representations from lower levels, thus enabling a shared learning process across tasks.

Architectural Components and Contributions

1. Model Architecture and Tasks:
   • The model uses neural networks, segmented into hierarchical layers supervised by individual tasks.
   • Each task—NER, EMD, RE, and CR—is encoded using bi-directional LSTMs and decoded through respective task-specific modules, such as sequence taggers or scoring layers.
2. Results and Benchmarks:
   • The approach yields superior results on NER, EMD, and RE tasks. These results suggest that shared representations can cohesively integrate linguistic elements crucial to diverse NLP applications.
   • Experimental evaluations confirm that a hierarchical task arrangement and multi-task supervision accelerate training and enhance model performance.
3. Sampling Strategy and Training:
   • The paper introduces proportional sampling, a simple yet potent strategy where task sampling probability aligns with dataset sizes, shown to be more effective than uniform sampling.
4. Embedding Insights and Linguistic Features:
   • By employing linguistic probing tasks, the paper examines the nature of hidden states and embeddings within the model. The insights affirm significant gains in encoding semantic information, a critical aspect of NLP complexity.

Implications and Future Directions

The paper contributes a significant step towards refining MTL in NLP, balancing simplicity and performance in model training without external dependencies. The hierarchical approach offers a structured method for handling task complexity, possibly influencing future research on task hierarchy and its impact on multi-task models. The implications extend into more efficient NLP solutions where shared representations significantly streamline the model's applicability to multiple tasks, thus optimizing computing resources and achieving faster convergence during training.

Future research could explore exploring other semantic task combinations, potential for transfer learning, and fine-tuning hierarchical structures to further capitalize on inter-task learning dynamics. Additionally, ongoing advancements in contextual embedding models, such as ELMo and beyond, could be leveraged to refine and augment the performance of hierarchical MTL paradigms in NLP.