Where It Really Matters: Few-Shot Environmental Conservation Media
  Monitoring for Low-Resource Languages
Sameer Jain Sedrick Scott Keh Shova Chettri Karun Dewan Pablo Izquierdo Johanna Prussman Pooja Shreshtha Cesar Suarez Zheyuan Ryan Shi Lei Li Fei Fang
Environmental conservation organizations routinely monitor news content on
conservation in protected areas to maintain situational awareness of
developments that can have an environmental impact. Existing automated media
monitoring systems require large amounts of data labeled by domain experts,
which is only feasible at scale for high-resource languages like English.
However, such tools are most needed in the global south where news of interest
is mainly in local low-resource languages, and far fewer experts are available
to annotate datasets sustainably. In this paper, we propose NewsSerow, a method
to automatically recognize environmental conservation content in low-resource
languages. NewsSerow is a pipeline of summarization, in-context few-shot
classification, and self-reflection using large language models (LLMs). Using
at most 10 demonstration example news articles in Nepali, NewsSerow
significantly outperforms other few-shot methods and achieves comparable
performance with models fully fine-tuned using thousands of examples. The World
Wide Fund for Nature (WWF) has deployed NewsSerow for media monitoring in
Nepal, significantly reducing their operational burden, and ensuring that AI
tools for conservation actually reach the communities that need them the most.
NewsSerow has also been deployed for countries with other languages like
Colombia.