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{this https URL}) and demo
website (\url{this http URL}). A demo video for FAMIE is
provided at: \url{this https URL}.

该论文介绍了 FAMIE，这是一个专为多语言信息提取而设计的全面且高效的主动学习工具包。FAMIE 基于使用小型代理网络进行快速数据选择的思想，引入了一种新的知识蒸馏机制，将代理网络与主大型模型（即基于 BERT 的模型）同步，以确保所选注释示例对于主模型的适当性。该主动学习框架支持多种语言，实验展示了以 FAMIE 为基础的主动学习在序列标注方面的竞争性能和时间效率优势。

FAMIE: 一个面向多语言信息抽取的快速主动学习框架

FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction

We present LOME, a system for performing multilingual information extraction.
Given a text document as input, our core system identifies spans of textual
entity and event mentions with a FrameNet (Baker et al., 1998) parser. It
subsequently performs coreference resolution, fine-grained entity typing, and
temporal relation prediction between events. By doing so, the system constructs
an event and entity focused knowledge graph. We can further apply third-party
modules for other types of annotation, like relation extraction. Our
(multilingual) first-party modules either outperform or are competitive with
the (monolingual) state-of-the-art. We achieve this through the use of
multilingual encoders like XLM-R (Conneau et al., 2020) and leveraging
multilingual training data. LOME is available as a Docker container on Docker
Hub. In addition, a lightweight version of the system is accessible as a web
demo.

本文介绍了 LOME 系统，用于执行多语言信息提取，通过 FrameNet 解析器标识文本实体和事件提及的跨度，执行指代消解、细粒度实体类型和事件之间的时间关系预测，构建事件和实体关注的知识图谱。LOME 可用作 Docker 容器和网络演示。