FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction

TL;DR

FAMIE is an efficient active learning framework for multilingual information extraction that uses a proxy network and knowledge distillation to accelerate annotation without sacrificing model performance.

Contribution

The paper introduces a novel active learning framework with a proxy network and knowledge distillation to improve annotation efficiency in multilingual information extraction.

Findings

FAMIE achieves competitive performance in sequence labeling tasks.

FAMIE significantly reduces annotation time compared to traditional methods.

The framework supports multiple languages effectively.

Abstract

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 (\url{https://github.com/nlp-uoregon/famie}) and demo website (\url{http://nlp.uoregon.edu:9000/}). A demo video for FAMIE is provided at: \url{https://youtu.be/I2i8n_jAyrY}.