Quantum Natural Language Processing for Fake News Identification

Description

Advancements in artificial intelligence, especially neural networks, have enabled more intelligent models that can distinguish between fake and real information. However, these models suffer from over-fitting: a phenomenon where models memorize certain patterns in the dataset instead of understanding the actual underlying task.This prevents the models from generalizing well, especially across domains. Quantum Natural Language Processing (QNLP) is a very nascent field where quantum computers solve NLP problems. It has been shown that QNLP models have been able to solve many of the aforementioned tasks difficult for neural networks to solve. This is attributed to the fact that QNLP models naturally incorporate rich linguistic meanings and structure. In this project we will create neural network like models for QNLP. This will be done on fact verification datasets, with the goal of improving the quality of fake news identification.

Awards

• Best Interdisciplinary Data Science Team

Students

• Sofian Ghazali

• Pasha Biglarzadeh

• Vignesh Selvaraj

• Yang Cheng

• Chenghong Hu

• Anish Kushalapa

• Jae Park

Advisors

• Mitch Paul Mithun

Skills Required by the team

• Python
• Deep Learning
• NLP