Leveraging Adapters and Prompt Tuning for Efficient Multi-Domain Named Entity Recognition in Low-Resource Settings

Concepts de base

This paper introduces a novel approach called Multi-BERT that leverages adapter modules and prompt tuning to enable a single core model to perform competitively across diverse text domains and output formats, even in low-resource settings.

Résumé

The paper presents a novel approach called Multi-BERT to address the challenges of multi-domain named entity recognition (NER) tasks. Key highlights:

Incorporates domain-specific parameters and output layers into a single core model, allowing it to adapt to diverse domains and output formats.
Utilizes techniques like prompt tuning and adapters to efficiently train the added parameters without compromising the base model.
Experiments on formal, informal, and noisy datasets show Multi-BERT outperforms existing practical models, even surpassing state-of-the-art in some cases.
Introduces a document-based domain detection pipeline to handle scenarios with unknown text domains, enhancing the model's adaptability.
Analysis of the adaptation strategies and their optimal hyperparameters for Persian NER settings.
The proposed approach demonstrates the ability to achieve outstanding results across all domains using a single model instance, addressing the limitations of traditional multi-model or unified model approaches.

Stats

The rapid expansion of text volume and diversity presents challenges in multi-domain NER settings.
Traditional approaches using a unified model or individual models per domain have significant limitations.
Single models struggle to capture nuances of diverse domains, while multiple large models lead to resource constraints.

Citations

"The dynamic nature of natural languages coupled with the diverse array of topics and contexts spanning different domains, presents a formidable challenge in NLP."
"No matter how good a trained model is, it will never be able to label the inputs perfectly, unless the model knows the context of the text."

Idées clés tirées de

Multi-BERT

by Parham Abed ... à arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02335.pdf

Questions plus approfondies

How can the proposed Multi-BERT approach be extended to handle dynamic changes in text domains over time?

The Multi-BERT approach can be extended to handle dynamic changes in text domains over time by implementing a continuous learning mechanism. This involves periodically updating the domain-specific parameters and adapting the model to new data as it becomes available. By incorporating a retraining schedule that includes regular updates based on the evolving text domains, the model can stay relevant and effective in capturing the nuances of changing contexts. Additionally, integrating a feedback loop system that continuously evaluates the model's performance on new data and adjusts the domain-specific parameters accordingly can help in maintaining adaptability to dynamic changes in text domains over time.

What are the potential challenges and limitations of the document-based domain detection pipeline, and how can it be further improved?

One potential challenge of the document-based domain detection pipeline is the accuracy of domain classification, especially when dealing with texts that contain elements from multiple domains. The pipeline may struggle to accurately assign a single domain label to complex or ambiguous texts. Additionally, the pipeline's performance may be impacted by the quality and quantity of training data available for domain classification. To improve the pipeline, incorporating more advanced natural language processing techniques such as contextual embeddings and ensemble learning methods can enhance the accuracy of domain detection. Furthermore, refining the training process by including a diverse range of text samples from various domains can help the model better generalize to unseen data and improve its overall performance.

Can the adapter and prompt tuning techniques used in Multi-BERT be applied to other NLP tasks beyond named entity recognition to achieve similar multi-domain adaptability?

Yes, the adapter and prompt tuning techniques utilized in Multi-BERT can be applied to other NLP tasks beyond named entity recognition to achieve similar multi-domain adaptability. These techniques can be effectively employed in tasks such as sentiment analysis, text classification, machine translation, and question answering, among others. By incorporating domain-specific adapters and task-specific prompts, models can be tailored to perform well across diverse domains without the need for separate models for each domain. This approach not only enhances adaptability but also improves efficiency and performance in handling multi-domain NLP tasks. Additionally, the flexibility of these techniques allows for customization based on the specific requirements of different tasks, making them versatile solutions for achieving multi-domain adaptability in various NLP applications.