Abstract

Abstract:

Multi-hop question answering (QA) requires reasoning over multiple documents to answer a complex question and provide explanations for the answer. Most existing QA systems use an end-to-end model to answer natural language questions, making it difficult to demonstrate that a model has performed the desired reasoning to predict the correct answer. As a result, most QA systems have fallen short of the goal of answering multi-hop questions in an interpretable fashion using an acceptable reasoning pattern. My research attempts to tackle two distinct problems in multi-hop QA: i) how to build an interpretable QA system to answer a complex multi-hop question; ii) how to learn knowledge for the new QA tasks while retaining the knowledge learned on previous QA tasks. The goal of this research is to help humans better understand and trust the mechanism of a QA system using multi-hop reasoning.

Bio:

I’m Zhenyun Deng, a postdoctoral research associate working with Prof. Andreas Vlachos on automated fact checking at the Department of Computer Science and Technology at the University of Cambridge. Before that, I received my PhD from the University of Auckland, New Zealand. My PhD research focuses on interpretable multi-hop question answering, which aims to reason over multiple documents to answer a complex question and provide explanations for the answer.