# Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment

arXiv: https://arxiv.org/abs/2006.03202 · HTML: https://mlsanity.com/paper/2006.03202/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [RSVP: Customer Intent Detection via Agent Response Contrastive and Generative Pre-Training](https://mlsanity.com/paper/2310.09773/) (2023)
- [Ensuring the Inclusive Use of Natural Language Processing in the Global Response to COVID-19](https://mlsanity.com/paper/2108.10791/) (2021)
- [Multi-View Zero-Shot Open Intent Induction from Dialogues: Multi Domain Batch and Proxy Gradient Transfer](https://mlsanity.com/paper/2303.13099/) (2023)

## Abstract (arXiv)

The spread of COVID-19 has become a significant and troubling aspect of society in 2020. With millions of cases reported across countries, new outbreaks have occurred and followed patterns of previously affected areas. Many disease detection models do not incorporate the wealth of social media data that can be utilized for modeling and predicting its spread. In this case, it is useful to ask, can we utilize this knowledge in one country to model the outbreak in another? To answer this, we propose the task of cross-lingual transfer learning for epidemiological alignment. Utilizing both macro and micro text features, we train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.