mlsanityData to 7 Oct 2026

FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction

arXiv 2202.08316 ↗First posted 16 Feb 2022cs.CLcited by 8

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Abstract (arXiv)

This paper presents FAMIE, a comprehensive and efficient active learning (AL) toolkit for multilingual information extraction. FAMIE is designed to address a fundamental problem in existing AL frameworks where annotators need to wait for a long time between annotation batches due to the time-consuming nature of model training and data selection at each AL iteration. This hinders the engagement, productivity, and efficiency of annotators. Based on the idea of using a small proxy network for fast data selection, we introduce a novel knowledge distillation mechanism to synchronize the proxy network with the main large model (i.e., BERT-based) to ensure the appropriateness of the selected annotation examples for the main model. Our AL framework can support multiple languages. The experiments demonstrate the advantages of FAMIE in terms of competitive performance and time efficiency for sequence labeling with AL. We publicly release our code (urlhttps://github.com/nlp-uoregon/famie) and demo website (urlhttp://nlp.uoregon.edu:9000/). A demo video for FAMIE is provided at: urlhttps://youtu.be/I2i8njAyrY.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 15 of its 40 references that are in the map. First posted on arXiv 16 Feb 2022.

Builds on

15 of 40 references.

References · 15

  1. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
  2. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  3. 2019Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Boundscited by 602
  4. 2016End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRFcited by 341
  5. 2020MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformerscited by 240
  6. 2019To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Taskscited by 167
  7. 2021Active Learning by Acquiring Contrastive Examplescited by 154
  8. 2017Deep Active Learning for Named Entity Recognitioncited by 108
  9. 2018Practical Obstacles to Deploying Active Learningcited by 68
  10. 2021Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processingcited by 68
  11. 2021Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimatescited by 39
  12. 2018One for All: Neural Joint Modeling of Entities and Eventscited by 37
3 more
  1. 2020Event Detection: Gate Diversity and Syntactic Importance Scoresfor Graph Convolution Neural Networkscited by 23
  2. 2021Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networkscited by 22
  3. 2020Deep Active Learning for Sequence Labeling Based on Diversity and Uncertainty in Gradientcited by 8

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 6; 2025: 1.

8 papers.

  1. 2024Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuningcited by 134
  2. 2022A Survey of Active Learning for Natural Language Processingcited by 40
  3. 2024ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarioscited by 17
  4. 2024Scoping Review of Active Learning Strategies and their Evaluation Environments for Entity Recognition Taskscited by 2
  5. 2024Few-shot Named Entity Recognition via Superposition Concept Discriminationcited by 1
  6. 2024Towards Efficient Active Learning in NLP via Pretrained Representationscited by 0
  7. 2024QueryBuilder: Human-in-the-Loop Query Development for Information Retrievalcited by 0
  8. 2025ATGen: A Framework for Active Text Generationcited by 0