NB-MLM: Efficient Domain Adaptation of Masked Language Models for Sentiment Analysis Article Swipe
Nikolay Arefyev
,
Dmitrii Kharchev
,
Artem Shelmanov
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2021.emnlp-main.717
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2021.emnlp-main.717
While Masked Language Models (MLM) are pre-trained on massive datasets, the additional training with the MLM objective on domain or task-specific data before fine-tuning for the final task is known to improve the final performance. This is usually referred to as the domain or task adaptation step. However, unlike the initial pre-training, this step is performed for each domain or task individually and is still rather slow, requiring several GPU days compared to several GPU hours required for the final task fine-tuning. We argue that the standard MLM objective leads to inefficiency when it is used for the adaptation step because it mostly learns to predict the most frequent words, which are not necessarily related to a final task. We propose a technique for more efficient adaptation that focuses on predicting words with large weights of the Naive Bayes classifier trained for the task at hand, which are likely more relevant than the most frequent words. The proposed method provides faster adaptation and better final performance for sentiment analysis compared to the standard approach.
Related Topics
Concepts
Computer science
Domain adaptation
Task (project management)
Inefficiency
Adaptation (eye)
Classifier (UML)
Artificial intelligence
Language model
Domain (mathematical analysis)
Sentiment analysis
Machine learning
Naive Bayes classifier
Natural language processing
Support vector machine
Microeconomics
Economics
Mathematics
Management
Physics
Optics
Mathematical analysis
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18653/v1/2021.emnlp-main.717
- https://aclanthology.org/2021.emnlp-main.717.pdf
- OA Status
- hybrid
- Cited By
- 11
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3212472206
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3212472206Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.18653/v1/2021.emnlp-main.717Digital Object Identifier
- Title
-
NB-MLM: Efficient Domain Adaptation of Masked Language Models for Sentiment AnalysisWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Nikolay Arefyev, Dmitrii Kharchev, Artem ShelmanovList of authors in order
- Landing page
-
https://doi.org/10.18653/v1/2021.emnlp-main.717Publisher landing page
- PDF URL
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https://aclanthology.org/2021.emnlp-main.717.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://aclanthology.org/2021.emnlp-main.717.pdfDirect OA link when available
- Concepts
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Computer science, Domain adaptation, Task (project management), Inefficiency, Adaptation (eye), Classifier (UML), Artificial intelligence, Language model, Domain (mathematical analysis), Sentiment analysis, Machine learning, Naive Bayes classifier, Natural language processing, Support vector machine, Microeconomics, Economics, Mathematics, Management, Physics, Optics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 3, 2023: 2, 2022: 2Per-year citation counts (last 5 years)
- References (count)
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18Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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