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Low-Resource Scenario in Natural Language Processing
The topic of low resource languages and domains has grown in popularity in recent years. One of the most significant open problems in NLP is the availability of large-scale data for training and improving data-hungry deep learning models and transformers in low-resource scenarios.
Low-resource scenarios include not only data unavailability caused by a threatened language, but also scenarios where the task is domain specific even when using a popular NLP language.
The current state of NLP: There is a significant opportunity to reach millions of speakers who do not have access to higher-level NLP applications.
In a Low resource scenario, different works assume various types of data scarcity. The dimensions of resource availability broadly classified as following. These categories aid in determining whether a particular method is applicable in a given low-resource situation.
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Task-specific Labels
- Most important dimension
- Created using Manual Annotation
- It is time intensive
- Requires experts for annotation
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Unlabeled Text
- Important for input embeddings and pre-trained language models
- It contains both language or domain-specific text
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Auxiliary Data
- Found in many forms like labels in different languages, knowledge bases, machine translations
- Absence of this data can make approaches non-applicable
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Natural language processing
Data Science
Foundations of Large Language Models Course
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An NLP team is developing a text summarization model for medical research papers in English. Although English is a high-resource language with vast amounts of general text available online, the team has only managed to collect 500 research papers with corresponding expert-written summaries. Which statement best analyzes this situation?
An NLP project is considered 'low-resource' when it suffers from data scarcity. Match each dimension of resource scarcity with its corresponding description and impact on model development.