2022/02/16 by Minh Van Nguyen, Nghia Trung Ngo, Van Nguyen, Minh +5 · 1 citation
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning and Algorithms #Topic Modeling #Web Data Mining and Analysis #cs.CL
paper · pdf · doi:10.48550/arxiv.2202.08316
Accepted to NAACL 2022 (System Demonstrations)
openalex publication_date 2022/02/16 · arxiv created 2022/05/04 · openalex created_date 2022/05/05 · arxiv updated 2022/05/06 · openalex updated_date 2026/07/28
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.