Add Noise, Tasks, or Layers? MaiNLP at the VarDial 2025 Shared Task on Norwegian Dialectal Slot and Intent Detection
This study compares noise injection, multi-task learning, and layer swapping for Norwegian dialect slot and intent detection, achieving up to 97.6% accuracy.
Key Findings
Methodology
The approach integrates multiple strategies: •Fine-tuning pre-trained language models (e.g., mDeBERTa, NorBERT, ScandiBERT) on various datasets; •Applying character-level noise to improve robustness; •Training on auxiliary tasks such as dialect ID, POS tagging, NER via multi-task or intermediate training; •Employing layer swapping, assembling layers from models fine-tuned on different data. Experiments show that combining English and dialectal data yields the most robust models, with top intent accuracy of 97.6% and slot F1 of 85.6%.
Key Results
- Character noise injection improved robustness, raising intent accuracy to 97.4% and slot F1 to 85.2%;
- Multi-task learning effects were mixed, with some auxiliary tasks negatively impacting slot filling;
- Layer swapping effectively combined models, enhancing generalization and robustness, especially in dialectal scenarios.
Significance
This research advances low-resource dialect NLP, demonstrating that combining noise, multi-task training, and layer assembly can significantly improve dialectal slot and intent recognition. It provides a framework for multilingual and low-resource NLP applications, addressing the challenge of dialectal variability and data scarcity, thus broadening the scope of practical NLP deployment in diverse linguistic contexts.
Technical Contribution
The study introduces a novel combination of noise injection, multi-task learning, and layer swapping, leveraging pre-trained models for cross-dialect transfer. The layer assembly technique, inspired by recent cross-lingual methods, allows effective model fusion, leading to performance gains of up to 97.6% intent accuracy. This approach offers a new paradigm for model robustness and transfer in low-resource settings.
Novelty
This is the first comprehensive evaluation of noise, multi-task learning, and layer swapping in Norwegian dialect SID. The innovative use of layer assembly to fuse models trained on different datasets is particularly novel, providing a new method for model ensembling that surpasses traditional approaches, especially in dialectal and low-resource contexts.
Limitations
- The models still struggle with extreme spelling variations and very limited data, indicating the need for more robust data augmentation;
- Layer swapping assumes compatible model architectures, limiting generalizability;
- Experiments are dataset-specific, and results may vary with other dialects or languages, requiring further validation.
Future Work
Future research will explore more sophisticated model fusion techniques, multi-modal data integration, and adaptation to other low-resource languages. Enhancing robustness against extreme variations and reducing computational costs are also key directions. Extending the framework to speech and multimodal inputs could further broaden its applicability.
AI Executive Summary
This study addresses the challenge of slot and intent detection (SID) in Norwegian dialects, a task complicated by data scarcity, spelling variability, and grammatical differences. Traditional models trained on standard languages perform poorly on dialectal data, necessitating innovative solutions. The researchers propose a multi-strategy framework combining noise injection, multi-task learning, and layer swapping to enhance model robustness and transferability.
They leverage pre-trained language models such as mDeBERTa, NorBERT, and ScandiBERT, fine-tuning them on various datasets, including English, machine-translated Norwegian, and dialectal corpora. Character-level noise is randomly injected into training data, simulating spelling variations and improving model resilience. Auxiliary tasks like dialect identification, POS tagging, and NER are trained either jointly or sequentially, aiming to enrich the models’ linguistic understanding.
A key innovation is the layer swapping technique, where layers from models fine-tuned on different datasets are assembled into a single model. This method effectively combines the strengths of multiple models, leading to significant performance improvements. Experimental results show that models trained on a mixture of English and dialectal data, with noise injection and layer assembly, achieve the highest scores, reaching 97.6% intent accuracy and 85.6% slot F1.
The findings demonstrate that such integrated approaches can substantially improve low-resource dialect NLP, with broad implications for multilingual NLP, speech recognition, and conversational AI. The research highlights the importance of model robustness, transfer learning, and innovative model fusion techniques in overcoming resource limitations.
Despite these advances, challenges remain in handling extreme spelling variations and ensuring model generalization across diverse dialects. Future work will focus on refining model fusion, expanding to multimodal data, and applying these techniques to other low-resource languages, aiming to democratize NLP technology for all linguistic communities.
Deep Analysis
Background
Natural language understanding (NLU)中的槽与意图识别（SID）已在标准语种中取得显著进展。近年来，低资源和方言场景引起关注，早期研究如Schuster等（2019）和Xu等（2020）利用大规模预训练模型实现高性能，但方言语料稀缺、拼写变异大，模型迁移困难。van der Goot等（2021）提出多任务学习框架，结合辅助任务提升鲁棒性。字符噪声和模型层拼接逐渐成为研究热点，旨在缓解低资源和跨方言迁移难题。本研究结合多策略，系统评估其在挪威方言中的应用，填补该领域空白。
Core Problem
挪威方言的槽与意图识别面临数据稀缺、拼写变异和语法差异等多重挑战。传统微调在标准语种表现优异，但在方言中效果有限，模型对拼写变异敏感。如何在低资源环境中提升识别准确率，成为关键问题。模型融合、鲁棒性和迁移能力不足限制实际应用。研究亟需探索多任务、多模型融合策略，以实现更强的适应性和泛化能力。
Innovation
核心创新包括：1）字符噪声注入技术，通过随机删除或插入字符增强模型对拼写变异的鲁棒性；2）多任务学习，结合方言识别、POS标注和NER，丰富模型理解能力；3）层交换技术，将不同微调模型的层拼接，形成融合多源信息的强大模型。这些创新突破传统单一微调限制，显著提升低资源方言识别性能，为多模型融合提供新思路。
Methodology
- �� 选择预训练模型：采用mDeBERTa、NorBERT、ScandiBERT作为基础模型；• 微调策略：在英语、挪威语机器翻译和方言语料上微调模型；• 噪声注入：随机删除或插入字符，增强拼写变异鲁棒性；• 多任务训练：同时进行SID、方言识别、POS、NER任务，或采用中间任务训练；• 层交换：拼接不同模型的前几层，形成新模型；• 训练细节：每个模型微调20轮，选择验证集最佳模型。
Experiments
使用xSID和NoMusic数据集，划分训练、验证、测试集。比较不同数据源对性能的影响，指标包括意图准确率和槽F1。通过字符噪声（10%、20%、30%）评估鲁棒性。引入多任务和中间任务训练，分析其效果。层交换实验拼接不同模型层，验证性能提升。多轮实验确保结果稳健。
Results
字符噪声显著提升鲁棒性，意图准确率最高97.4%，槽F1达85.2%；多任务学习效果复杂，部分任务影响槽填充；层交换拼接模型层，增强泛化能力，表现优于单一模型。结合英语和少量方言数据训练的模型在共享任务中表现最佳，意图97.6%，槽85.6%。
Applications
该技术适用于多语种智能问答、语音识别、对话系统，特别在低资源和方言场景中。增强模型鲁棒性和迁移能力，提升识别准确率和用户体验。未来结合多模态信息，拓展到多语种、多方言，推动多语言交互技术发展。
Limitations & Outlook
模型在极端拼写变异或极少数据的方言中表现仍有限，鲁棒性需提升。层交换依赖模型层级结构一致性，限制适用范围。实验主要在特定数据集验证，泛化到其他低资源语言和方言仍需验证。未来需优化融合策略，降低计算成本，增强泛化能力。
Plain Language Accessible to non-experts
想象你在一家工厂工作，工厂里有许多不同的机器（模型），每台机器都能完成特定任务，比如装配、包装。有时候，工厂遇到拼写错误或不同的操作方式（方言变异），让机器难以识别。为了让工厂更聪明，你可以在机器的输入中加入一些“干扰”（噪声），让它学会应对不同情况。你还可以让不同的机器合作，比如一台识别方言，一台检查拼写，然后把它们的部分拼接，组成一台超级机器。这样，工厂的生产效率就会大大提高，能应对各种复杂情况。这就像论文中用字符噪声、多任务学习和层交换的方法，让模型变得更强大、更灵活，能更好理解挪威方言中的槽和意图。
ELI14 Explained like you're 14
想象你在学校学外语，有时候老师用不同的口音或拼写说话，你可能听不懂。这个研究就像在帮你训练一个超级翻译机，不仅能理解标准英语，还能理解各种方言和拼写变化。科学家用一些聪明的方法，比如在训练时加入“干扰”让机器更坚强，或者让它同时学习相关技能，比如识别方言或句子结构。还有一种特别的方法是，把不同机器的“脑袋”拼在一起，组成一个更厉害的“超级脑袋”。这些技术让机器更聪明，能在面对不同方言时表现出色。最终，这个系统能准确理解用户意图，就像你用不同口音和拼写跟朋友聊天一样自然。
Abstract
Slot and intent detection (SID) is a classic natural language understanding task. Despite this, research has only more recently begun focusing on SID for dialectal and colloquial varieties. Many approaches for low-resource scenarios have not yet been applied to dialectal SID data, or compared to each other on the same datasets. We participate in the VarDial 2025 shared task on slot and intent detection in Norwegian varieties, and compare multiple set-ups: varying the training data (English, Norwegian, or dialectal Norwegian), injecting character-level noise, training on auxiliary tasks, and applying Layer Swapping, a technique in which layers of models fine-tuned on different datasets are assembled into a model. We find noise injection to be beneficial while the effects of auxiliary tasks are mixed. Though some experimentation was required to successfully assemble a model from layers, it worked surprisingly well; a combination of models trained on English and small amounts of dialectal data produced the most robust slot predictions. Our best models achieve 97.6% intent accuracy and 85.6% slot F1 in the shared task.