mlsanityData to 7 Oct 2026

Zero and Few-shot Learning for Author Profiling

arXiv 2204.10543 ↗First posted 22 Apr 2022cs.CLcited by 4

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Abstract (arXiv)

Author profiling classifies author characteristics by analyzing how language is shared among people. In this work, we study that task from a low-resource viewpoint: using little or no training data. We explore different zero and few-shot models based on entailment and evaluate our systems on several profiling tasks in Spanish and English. In addition, we study the effect of both the entailment hypothesis and the size of the few-shot training sample. We find that entailment-based models out-perform supervised text classifiers based on roberta-XLM and that we can reach 80% of the accuracy of previous approaches using less than 50% of the training data on average.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 16 of its 37 references that are in the map. First posted on arXiv 22 Apr 2022.

Builds on

16 of 37 references.

References · 16

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  5. 2015A large annotated corpus for learning natural language inferencecited by 1,837
  6. 2020Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inferencecited by 934
  7. 2019Adversarial NLI: A New Benchmark for Natural Language Understandingcited by 798
  8. 2019Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approachcited by 299
  9. 2017Efficient Natural Language Response Suggestion for Smart Replycited by 228
  10. 2021Entailment as Few-Shot Learnercited by 122
  11. 2020Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Startcited by 44
  12. 2022Few-Shot Learning with Siamese Networks and Label Tuningcited by 15
4 more
  1. 2019An Emotional Analysis of False Information in Social Media and News Articlescited by 12
  2. 2021Emotion Ratings: How Intensity, Annotation Confidence and Agreements are Entangledcited by 10
  3. 2020Unsupervised Label Refinement Improves Dataless Text Classificationcited by 9
  4. 2017A Low Dimensionality Representation for Language Variety Identificationcited by 2

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 1.

4 papers.

  1. 2022PART: Pre-trained Authorship Representation Transformercited by 13
  2. 2023Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language Understandingcited by 8
  3. 2024Prompt-based Personality Profiling: Reinforcement Learning for Relevance Filteringcited by 0
  4. 2025A Computational Framework to Identify Self-Aspects in Textcited by 0