Domain Adaptation in NLP: Enhancing model performance in new domains by leveraging existing knowledge.
Natural Language Processing (NLP) models often struggle when applied to out-of-distribution examples or new domains. Domain adaptation aims to improve a model"s performance in a target domain by leveraging knowledge from a source domain. This article explores the nuances, complexities, and current challenges in domain adaptation for NLP, discussing recent research and future directions.
Gradual fine-tuning, as demonstrated by Haoran Xu et al., can yield substantial gains in low-resource domain adaptation without modifying the model or learning objective. Eyal Ben-David and colleagues introduced 'domain adaptation from scratch,' a learning setup that efficiently annotates data from source domains to perform well on a sensitive target domain, where data is unavailable for annotation. This approach has shown promising results in sentiment analysis and Named Entity Recognition tasks.
Yusuke Watanabe and co-authors proposed a simple domain adaptation method for neural networks in a supervised setting, which outperforms other domain adaptation methods on captioning datasets. Eyal Ben-David et al. also developed PERL, a pivot-based fine-tuning model that extends contextualized word embedding models like BERT, achieving improved performance across various sentiment classification domain adaptation setups.
In the biomedical NLP field, Usman Naseem and colleagues presented BioALBERT, a domain-specific adaptation of ALBERT trained on biomedical and clinical corpora. BioALBERT outperforms the state of the art in various tasks, such as named entity recognition, relation extraction, sentence similarity, document classification, and question answering.
Legal NLP tasks have also been explored, with Saibo Geng et al. investigating the value of domain adaptive pre-training and language adapters. They found that domain adaptive pre-training is most helpful with low-resource downstream tasks, and adapters can yield similar performance to full model tuning with much smaller training costs.
Xu Guo and Han Yu provided a comprehensive survey on domain adaptation and generalization of pretrained language models (PLMs), proposing a taxonomy of domain adaptation approaches covering input augmentation, model optimization, and personalization. They also discussed and compared various methods, suggesting promising future research directions.
In the context of information retrieval, Vaishali Pal and co-authors studied parameter-efficient sparse retrievers and rerankers using adapters. They found that adapters not only retain efficiency and effectiveness but are also memory-efficient and lighter to train compared to fully fine-tuned models.
Practical applications of domain adaptation in NLP include sentiment analysis, named entity recognition, and information retrieval. A company case study is BioALBERT, which has set a new state of the art in 17 out of 20 benchmark datasets for biomedical NLP tasks. By connecting domain adaptation to broader theories, researchers can continue to develop innovative methods to improve NLP model performance in new domains.
Domain Adaptation in NLP
Domain Adaptation in NLP Further Reading1.Gradual Fine-Tuning for Low-Resource Domain Adaptation http://arxiv.org/abs/2103.02205v2 Haoran Xu, Seth Ebner, Mahsa Yarmohammadi, Aaron Steven White, Benjamin Van Durme, Kenton Murray2.Domain Adaptation from Scratch http://arxiv.org/abs/2209.00830v1 Eyal Ben-David, Yftah Ziser, Roi Reichart3.Domain Adaptation for Neural Networks by Parameter Augmentation http://arxiv.org/abs/1607.00410v1 Yusuke Watanabe, Kazuma Hashimoto, Yoshimasa Tsuruoka4.PERL: Pivot-based Domain Adaptation for Pre-trained Deep Contextualized Embedding Models http://arxiv.org/abs/2006.09075v1 Eyal Ben-David, Carmel Rabinovitz, Roi Reichart5.Neural Structural Correspondence Learning for Domain Adaptation http://arxiv.org/abs/1610.01588v3 Yftah Ziser, Roi Reichart6.Benchmarking for Biomedical Natural Language Processing Tasks with a Domain Specific ALBERT http://arxiv.org/abs/2107.04374v1 Usman Naseem, Adam G. Dunn, Matloob Khushi, Jinman Kim7.Legal Transformer Models May Not Always Help http://arxiv.org/abs/2109.06862v2 Saibo Geng, Rémi Lebret, Karl Aberer8.On the Domain Adaptation and Generalization of Pretrained Language Models: A Survey http://arxiv.org/abs/2211.03154v1 Xu Guo, Han Yu9.Parameter-Efficient Sparse Retrievers and Rerankers using Adapters http://arxiv.org/abs/2303.13220v1 Vaishali Pal, Carlos Lassance, Hervé Déjean, Stéphane Clinchant10.Pre-train or Annotate? Domain Adaptation with a Constrained Budget http://arxiv.org/abs/2109.04711v3 Fan Bai, Alan Ritter, Wei Xu
Domain Adaptation in NLP Frequently Asked Questions
What is domain adaptation in NLP?
Domain adaptation in Natural Language Processing (NLP) refers to the process of enhancing a model's performance in a new domain (target domain) by leveraging knowledge from an existing domain (source domain). This technique is particularly useful when dealing with out-of-distribution examples or when applying NLP models to new domains where the available data is limited or scarce.
What is meant by domain adaptation?
Domain adaptation is a machine learning technique that aims to improve the performance of a model on a specific target domain by utilizing knowledge and information from a related source domain. This approach is useful when there is limited labeled data available in the target domain, and it helps to overcome the challenges of data scarcity and distribution shift between domains.
What are the domains of NLP?
In the context of NLP, domains refer to different areas or fields where language is used, such as news articles, social media, biomedical texts, legal documents, or customer reviews. Each domain has its unique characteristics, vocabulary, and style, which can affect the performance of NLP models when applied to new or unseen domains.
What is the difference between domain transfer and domain adaptation?
Domain transfer and domain adaptation are related concepts in machine learning. Domain transfer refers to the process of applying a model trained on one domain (source domain) to a different domain (target domain) without any modification or fine-tuning. In contrast, domain adaptation involves adjusting or fine-tuning the model to improve its performance on the target domain by leveraging knowledge from the source domain.
What are some recent advancements in domain adaptation for NLP?
Recent advancements in domain adaptation for NLP include gradual fine-tuning, domain adaptation from scratch, pivot-based fine-tuning models like PERL, and domain-specific adaptations of pretrained models like BioALBERT. These approaches have shown promising results in various NLP tasks, such as sentiment analysis, named entity recognition, relation extraction, and information retrieval.
How does domain adaptation help in low-resource settings?
Domain adaptation helps in low-resource settings by leveraging knowledge from a related source domain with abundant data to improve the performance of a model on a target domain with limited labeled data. This approach allows the model to generalize better and overcome the challenges of data scarcity and distribution shift between domains.
What are some practical applications of domain adaptation in NLP?
Practical applications of domain adaptation in NLP include sentiment analysis, named entity recognition, information retrieval, relation extraction, sentence similarity, document classification, and question answering. These techniques have been applied in various fields, such as biomedical NLP, legal NLP, and customer review analysis, to improve model performance and adapt to new domains.
What are the future research directions in domain adaptation for NLP?
Future research directions in domain adaptation for NLP include exploring new methods for input augmentation, model optimization, and personalization, as well as investigating the value of domain adaptive pre-training and language adapters. Researchers can also focus on developing more efficient and memory-efficient models, such as sparse retrievers and rerankers using adapters, to further improve NLP model performance in new domains.
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