Transfer Learning-based Natural Language Processing (NLP) Task

A Transfer Learning-based Natural Language Processing (NLP) Task is a data-driven NLP task that is a transfer learning task (which that leverages knowledge learned from one or more source domains to improve the learning efficiency or performance in a different but related target domain).

• AKA: Domain Adaptation NLP Task.
• Context:
  • It can be solved by a Transfer Learning NLP System (that implements a transfer learning NLP algorithm).
  • It can range from being a Transfer Learning-based Natural Language Understanding Task, to being a Transfer Learning-based Natural Language Generation Task.
  • It can range from being a Zero-Shot NLP Task to being a Few-Shot NLP Task.
  • ...
• Example(s):
  • a In-Context NLP Task, where _____.
  • a Transfer Learning-based NLP Benchmark Learning Task.
  • a Transfer Learning-based End-of-Sentence Detection Task, where ____.
  • a Transfer Learning-based Missing Word Prediction Task, where ____.
  • a Transfer Learning-based Named Entity Recognition Task, where ____.
  • a Transfer Learning-based Information Extraction Task, where ____.
  • a Transfer Learning-based Document Summarization Task, where ____.
  • a Transfer Learning-based Question Answering Task, where ____.
  • ...
• Counter-Example(s):
  • Single Domain NLP Task, where learning and inference occur within the same domain.
  • Zero-Shot Learning NLP Task, where the system is expected to perform tasks it has not seen during training.
  • Supervised NLP Task, where the system is trained and tested on the same task with a large amount of labeled data.
  • ...
• See: NLP Task, Transfer Learning, Domain Adaptation, Source Domain, Target Domain.

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