Harnessing Unsupervised Word Translation to Address Resource Inequality for Peace and Health

Research geared toward human well-being in developing nations often concentrates on web content written in a world language (e.g., English) and ignores a significant chunk of content written in a poorly resourced yet highly prevalent first language of the region in concern (e.g., Hindi). Such omissions are common due to the sheer mismatch between linguistic resources offered in a world language and its low-resource counterpart. However, during a global pandemic or an imminent war, demand for linguistic resources might get recalibrated. In this work, we focus on the high-resource and low-resource language pair ⟨ en, hie⟩ (English, and Romanized Hindi) and present a cross-lingual sampling method that takes example documents in English, and retrieves similar content written in Romanized Hindi, the most popular form of Hindi observed in social media. At the core of our technique is a novel finding that a surprisingly simple constrained nearest-neighbor sampling in polyglot Skip-gram word embedding space can retrieve substantial bilingual lexicons, even from harsh social media data sets. Our cross-lingual sampling method obtains substantial performance improvement in the important domains of detecting peace-seeking, hostility-diffusing hope speech in the context of the 2019 India-Pakistan conflict, and in detecting comments encouraging compliance with COVID-19 guidelines. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Empathy and Hope: Resource Transfer to Model Inter-country Social Media Dynamics

The ongoing COVID-19 pandemic resulted in significant ramifications for international relations ranging from travel restrictions, global ceasefires, and international vaccine production and sharing agreements. Amidst a wave of infections in India that resulted in a systemic breakdown of healthcare infrastructure, a social welfare organization based in Pakistan offered to procure medical-grade oxygen to assist India - a nation which was involved in four wars with Pakistan in the past few decades. In this paper, we focus on Pakistani Twitter users' response to the ongoing healthcare crisis in India. While #IndiaNeedsOxygen and #PakistanStandsWithIndia featured among the toptrending hashtags in Pakistan, divisive hashtags such as #EndiaSaySorryToKashmir simultaneously started trending. Against the back-drop of a contentious history including four wars, divisive content of this nature, especially when a country is facing an unprecedented healthcare crisis, fuels further deterioration of relations. In this paper, we define a new task of detecting supportive content and demonstrate that existing NLP for social impact tools can be effectively harnessed for such tasks within a quick turnaround time. We also release the first publicly available data set(1) at the intersection of geopolitical relations and a raging pandemic in the context of India and Pakistan.