Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks (1703.06345v1)

Abstract: Recent papers have shown that neural networks obtain state-of-the-art performance on several different sequence tagging tasks. One appealing property of such systems is their generality, as excellent performance can be achieved with a unified architecture and without task-specific feature engineering. However, it is unclear if such systems can be used for tasks without large amounts of training data. In this paper we explore the problem of transfer learning for neural sequence taggers, where a source task with plentiful annotations (e.g., POS tagging on Penn Treebank) is used to improve performance on a target task with fewer available annotations (e.g., POS tagging for microblogs). We examine the effects of transfer learning for deep hierarchical recurrent networks across domains, applications, and languages, and show that significant improvement can often be obtained. These improvements lead to improvements over the current state-of-the-art on several well-studied tasks.

• The paper introduces a hierarchical RNN framework that leverages transfer learning to improve sequence tagging in low-resource scenarios.
• The authors propose three architectures with varying parameter sharing, yielding significant performance gains across cross-domain and multilingual datasets.
• Empirical evaluations demonstrate up to 9% improvement over state-of-the-art benchmarks, underscoring the model’s robustness and versatility.

Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks

The paper "Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks" by Zhilin Yang, Ruslan Salakhutdinov, and William W. Cohen presents a comprehensive paper on the application of transfer learning techniques to sequence tagging tasks using deep hierarchical recurrent neural networks (RNNs). The primary focus is on leveraging annotated data from a well-resourced source task to enhance performance on a target task with limited annotations. This paper explores cross-domain, cross-application, and cross-lingual transfer learning settings, demonstrating performance improvements over existing state-of-the-art results.

Key Contributions

1. Hierarchical RNN Framework for Transfer Learning:
   • The authors develop a base model for sequence tagging, comprising hierarchical RNNs that process character-level and word-level features. The model uses gated recurrent units (GRUs) to capture morphological and contextual information from sequences.
   • Three distinct transfer learning architectures (T-A, T-B, T-C) are proposed to cater to different transfer scenarios, with varying degrees of parameter sharing between source and target tasks.
2. Empirical Evaluations:
   • Extensive experiments validate the efficacy of the transfer learning techniques across multiple datasets, languages, and applications.
   • Results show significant gains in performance, especially when the target task is low-resource, showcasing the utility of shared representations in hierarchical RNNs.
3. Comparison with State-of-the-Art:
   • The proposed models achieve new state-of-the-art performance on several benchmark datasets, indicating the effectiveness of transfer learning with hierarchical RNNs in sequence tagging tasks.

Experimental Details

• Datasets: The paper evaluates the approach on diverse datasets, including the Penn Treebank (PTB) for POS tagging, CoNLL datasets for chunking and NER in multiple languages, the Genia corpus, and a Twitter corpus.
• Low-Resource Settings: To simulate low-resource conditions, the authors introduce varying labeling rates to evaluate the robustness of their models.
• Model Implementation: GRUs are employed for both character-level and word-level networks, with adaptations in the objective function using a max-margin principle to enhance learning efficiency.

Numerical Results

The results indicate substantial improvements in sequence tagging performance, particularly under low-labeling conditions. The transfer learning strategies show notable improvements:

• Cross-domain and cross-application transfers yield significant gains, with benchmarks demonstrating improvements of up to 9% in low-resource configurations.
• Cross-lingual transfers between languages with similar alphabets achieve meaningful enhancements, reflecting the potential for multilingual applications without additional linguistic resources.

Implications and Future Directions

This research underscores the importance of transfer learning in enhancing sequence tagging tasks, particularly in challenging low-resource environments. The paper also demonstrates the versatility of hierarchical RNNs in capturing transferable features across different contexts. Future work could integrate resource-based transfer methods to further augment cross-lingual transfer learning. Developing more sophisticated parameter-sharing strategies could unlock additional performance gains in even broader application settings.

In conclusion, this paper makes significant contributions to the field of natural language processing by advancing the transfer learning paradigm through hierarchical recurrent architectures, offering promising directions for research and application in both multilingual and low-resource scenarios.