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Zero-Shot Learning in NLP: Techniques for Generalizing to Unseen Tasks and Domains

EasyChair Preprint no. 12263

8 pagesDate: February 24, 2024


Zero-shot learning (ZSL) in Natural Language Processing (NLP) is a burgeoning field aimed at enabling models to generalize to tasks and domains not seen during training. This paper explores various techniques and strategies employed to achieve such generalization. Traditional NLP models often struggle when confronted with unseen tasks or domains due to their reliance on annotated data. ZSL approaches address this limitation by leveraging auxiliary information such as semantic embeddings, ontologies, or textual descriptions to bridge the gap between seen and unseen classes. By embracing ZSL techniques, NLP practitioners can enhance the adaptability and robustness of their models, thereby advancing the frontier of natural language understanding and generation.

Keyphrases: language, natural, processing

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Kurez Oroy and Chris Liu},
  title = {Zero-Shot Learning in NLP: Techniques for Generalizing to Unseen Tasks and Domains},
  howpublished = {EasyChair Preprint no. 12263},

  year = {EasyChair, 2024}}
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