LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting (2109.00720v5)

Abstract: Most NER methods rely on extensive labeled data for model training, which struggles in the low-resource scenarios with limited training data. Existing dominant approaches usually suffer from the challenge that the target domain has different label sets compared with a resource-rich source domain, which can be concluded as class transfer and domain transfer. In this paper, we propose a lightweight tuning paradigm for low-resource NER via pluggable prompting (LightNER). Specifically, we construct the unified learnable verbalizer of entity categories to generate the entity span sequence and entity categories without any label-specific classifiers, thus addressing the class transfer issue. We further propose a pluggable guidance module by incorporating learnable parameters into the self-attention layer as guidance, which can re-modulate the attention and adapt pre-trained weights. Note that we only tune those inserted module with the whole parameter of the pre-trained LLM fixed, thus, making our approach lightweight and flexible for low-resource scenarios and can better transfer knowledge across domains. Experimental results show that LightNER can obtain comparable performance in the standard supervised setting and outperform strong baselines in low-resource settings. Code is in https://github.com/zjunlp/DeepKE/tree/main/example/ner/few-shot.

• The paper introduces a generative NER framework that replaces label-specific classifiers with a unified learnable verbalizer.
• It employs a pluggable guidance module integrated into self-attention layers to enhance both domain and class transfer.
• Experimental results demonstrate that LightNER outperforms traditional models in low-resource and cross-domain settings.

Insightful Overview of "LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting"

The paper "LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting" addresses the persistent challenge faced by Named Entity Recognition (NER) in low-resource settings. Traditional NER models struggle with insufficient labeled data and often require significant reconfiguration when applied to new domains with unseen categories. This research introduces a novel approach called LightNER, which aims to mitigate these issues through a lightweight and flexible tuning mechanism.

Key Contributions

LightNER is an innovative framework that seeks to improve low-resource NER by focusing on two critical issues: class transfer and domain transfer. The authors propose a generative framework that eliminates the need for label-specific classifiers, allowing for more seamless adaptation to new classes. Additionally, the incorporation of a pluggable guidance module serves to enhance the transferability of pre-trained LLMs (PLMs) across different domains.

Key Contributions:

1. Unified Learnable Verbalizer:
   • LightNER reformulates the conventional sequence labeling task into a generative framework. This involves constructing a "unified learnable verbalizer" that serves as a decoupling space, bypassing the traditional label-specific output layers. This approach allows the model to handle class transfer without architectural changes, facilitating easier adaptation to domains with new entity categories.
2. Pluggable Guidance Module:
   • A pluggable guidance module is introduced, which incorporates learnable parameters into the self-attention layers of the model. This module modulates the attention mechanism, promoting effective domain knowledge transfer without altering the entire parameter set of the PLM. This design maintains computational efficiency and adaptability.

Experimental Evaluation

The experiments conducted demonstrate that LightNER achieves commendable results under both standard and low-resource conditions. Particularly noteworthy is the framework’s ability to outperform established baselines in various cross-domain scenarios:

• In the low-resource setting, LightNER significantly surpasses traditional models such as LC-BERT and TEMPLATE-based approaches, especially when entities in the target domain differ from those in the training domain.
• Consistent improvements across domains indicate that the pluggable module, although lightweight, effectively bridges the gap between pre-trained knowledge and new domain requirements.

Implications and Future Directions

The implications of this research are multifaceted, offering theoretical and practical advancements in NER. The paradigm shift from sequence labeling to a generative framework, combined with the lightweight tuning introduced by the pluggable module, paves the way for more versatile and resource-efficient NER implementations.

Theoretical Implications:

• This research challenges the conventional reliance on extensive parameter tuning across different domains, advocating for a decoupled approach that leverages verbalizers and modular tuning.

Practical Implications:

• Practitioners can implement LightNER to rapidly deploy NER models in settings where labeled data is scarce, thereby reducing the need for costly and time-consuming data annotation processes.

Future Directions:

• Exploring the integration of external knowledge sources such as knowledge graphs may further enhance LightNER's ability to transfer knowledge across domains.
• Investigating the effectiveness of the framework in more diverse linguistic environments could validate its robustness and applicability in multilingual settings.

In conclusion, LightNER represents a significant stride toward efficient and flexible NER solutions in low-resource environments. By promoting innovations in tunable modeling and transfer learning, this paper contributes valuable insights that can shape the future of automated information extraction systems.