This talk is part of the NLP Seminar Series.

Political Polarization and International Conflicts through the Lens of NLP

Ashique KhudaBukhsh, Carnegie Mellon University
Date: 10:00am - 11:00am PT, Feb 25 2021
Venue: Zoom (link hidden)


In this talk, I will summarize two broad lines of NLP research focusing on (1) the current US political crisis and (2) the long-standing international conflict between the two nuclear adversaries India and Pakistan.

The first part of the talk presents a new methodology that offers a fresh perspective on interpreting and understanding political polarization through machine translation. We begin with a novel proposition that two sub-communities viewing different US cable news networks are speaking in two different languages. Next, we demonstrate that with this assumption, modern machine translation methods can provide a simple yet powerful and interpretable framework to understand the differences between two (or more) large-scale social media discussion data sets at the granularity of words.

The second part of the talk seeks to examine what we term as hostility-diffusing, peace seeking hope speech in the context of the 2019 India-Pakistan conflict. In doing so, we tackle several practical challenges that arise from multilingual texts and demonstrate how novel methods can effectively extend linguistic resources (e.g., content classifier, labeled examples) from a world language (e.g., English) to a low-resource language (e.g., Hindi). To this end, we show two different approaches -- one relying on code switching and the other relying on unsupervised machine translation -- which achieve substantial improvement in detecting Hindi hope speech under low-supervision settings.


Ashique KhudaBukhsh is currently a Project Scientist at the Language Technologies Institute, Carnegie Mellon University (CMU) mentored by Prof. Tom Mitchell. Prior to this role, he was a postdoc mentored by Prof. Jaime Carbonell at CMU. His PhD thesis (Computer Science Department, CMU, also advised by Prof. Jaime Carbonell) focused on distributed active learning. His current research lies at the intersection of NLP and AI for Social Impact. In this field, he is interested in analyzing globally important events in South East Asia and developing methods for noisy social media texts generated in this linguistically diverse region. His other broad research focus is US politics; in this area, his research involves devising novel methods to quantify, interpret and understand political polarization.