# SPEED++: A Multilingual Event Extraction Framework for Epidemic Prediction and Preparedness arXiv: https://arxiv.org/abs/2410.18393 ยท HTML: https://mlsanity.com/paper/2410.18393/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 63 references that are in the map. - First posted on arXiv 24 Oct 2024. ## Builds on (most cited first, 30 of 30) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023) - [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019) - [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019) - [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020) - [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019) - [Event Extraction by Answering (Almost) Natural Questions](https://mlsanity.com/paper/2004.13625/) (2020) - [Document-Level Event Argument Extraction by Conditional Generation](https://mlsanity.com/paper/2104.05919/) (2021) - [MAVEN: A Massive General Domain Event Detection Dataset](https://mlsanity.com/paper/2004.13590/) (2020) - [DEGREE: A Data-Efficient Generation-Based Event Extraction Model](https://mlsanity.com/paper/2108.12724/) (2021) - [Cross-media Structured Common Space for Multimedia Event Extraction](https://mlsanity.com/paper/2005.02472/) (2020) - [Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection](https://mlsanity.com/paper/1910.11621/) (2019) - [Multilingual Generative Language Models for Zero-Shot Cross-Lingual Event Argument Extraction](https://mlsanity.com/paper/2203.08308/) (2022) - [TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction](https://mlsanity.com/paper/2311.09562/) (2023) - [GENEVA: Benchmarking Generalizability for Event Argument Extraction with Hundreds of Event Types and Argument Roles](https://mlsanity.com/paper/2205.12505/) (2022) - [AMPERE: AMR-Aware Prefix for Generation-Based Event Argument Extraction Model](https://mlsanity.com/paper/2305.16734/) (2023) - [MEE: A Novel Multilingual Event Extraction Dataset](https://mlsanity.com/paper/2211.05955/) (2022) - [Code-switched Language Models Using Dual RNNs and Same-Source Pretraining](https://mlsanity.com/paper/1809.01962/) (2018) - [TAGPRIME: A Unified Framework for Relational Structure Extraction](https://mlsanity.com/paper/2205.12585/) (2022) - [Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments](https://mlsanity.com/paper/2212.09683/) (2022) - [Weibo-COV: A Large-Scale COVID-19 Social Media Dataset from Weibo](https://mlsanity.com/paper/2005.09174/) (2020) - [Discourse-level Relation Extraction via Graph Pooling](https://mlsanity.com/paper/2101.00124/) (2021) - [Extracting a Knowledge Base of COVID-19 Events from Social Media](https://mlsanity.com/paper/2006.02567/) (2020) - [Improving Event Definition Following For Zero-Shot Event Detection](https://mlsanity.com/paper/2403.02586/) (2024) - [Event Detection from Social Media for Epidemic Prediction](https://mlsanity.com/paper/2404.01679/) (2024) - [Dual Language Models for Code Switched Speech Recognition](https://mlsanity.com/paper/1711.01048/) (2017) - [ExcavatorCovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID-19](https://mlsanity.com/paper/2105.01819/) (2021) - [QUDSELECT: Selective Decoding for Questions Under Discussion Parsing](https://mlsanity.com/paper/2408.01046/) (2024) - [GenEARL: A Training-Free Generative Framework for Multimodal Event Argument Role Labeling](https://mlsanity.com/paper/2404.04763/) (2024) ## Built on by (most cited first, 7 of 7) - [Instruction-Tuning LLMs for Event Extraction with Annotation Guidelines](https://mlsanity.com/paper/2502.16377/) (2025) - [Event Extraction in Large Language Model](https://mlsanity.com/paper/2512.19537/) (2025) - [Dynamic Strategy Planning for Efficient Question Answering with Large Language Models](https://mlsanity.com/paper/2410.23511/) (2024) - [SNaRe: Domain-aware Data Generation for Low-Resource Event Detection](https://mlsanity.com/paper/2502.17394/) (2025) - [Multimodal Event Detection: Current Approaches and Defining the New Playground through LLMs and VLMs](https://mlsanity.com/paper/2505.10836/) (2025) - [PExA: Parallel Exploration Agent for Complex Text-to-SQL](https://mlsanity.com/paper/2604.22934/) (2026) - [Calibrated Typed Decisions for Entity, Relation, and Event Extraction](https://mlsanity.com/paper/2610.17113/) (2026) ## Abstract (arXiv) Social media is often the first place where communities discuss the latest societal trends. Prior works have utilized this platform to extract epidemic-related information (e.g. infections, preventive measures) to provide early warnings for epidemic prediction. However, these works only focused on English posts, while epidemics can occur anywhere in the world, and early discussions are often in the local, non-English languages. In this work, we introduce the first multilingual Event Extraction (EE) framework SPEED++ for extracting epidemic event information for a wide range of diseases and languages. To this end, we extend a previous epidemic ontology with 20 argument roles; and curate our multilingual EE dataset SPEED++ comprising 5.1K tweets in four languages for four diseases. Annotating data in every language is infeasible; thus we develop zero-shot cross-lingual cross-disease models (i.e., training only on English COVID data) utilizing multilingual pre-training and show their efficacy in extracting epidemic-related events for 65 diverse languages across different diseases. Experiments demonstrate that our framework can provide epidemic warnings for COVID-19 in its earliest stages in Dec 2019 (3 weeks before global discussions) from Chinese Weibo posts without any training in Chinese. Furthermore, we exploit our framework's argument extraction capabilities to aggregate community epidemic discussions like symptoms and cure measures, aiding misinformation detection and public attention monitoring. Overall, we lay a strong foundation for multilingual epidemic preparedness.