Question-to-question semantic similarity for Question Answering System

Question Answering (QA) system automatically answer questions raised by users in natural languages, and it is a crucial component of a human-machine conversation system. A typical QA system collects human written question-answer groups and structures them in a database system. However, in order to answer questions that are semantically similar to the questions stored in the database but are worded differently, the QA system needs to be able to calculate the semantic similarity between different questions. In this research project, the intern will explore different techniques used in question-to-question semantic similarity measurement and try to improve upon the state- of-the-art performance. From participating in this project, RSVP Technology Inc. could seed for more opportunities to collaborate with Canadian community to improve the quality of QA systems used in many other fields and products, such as customer service chatbots and smart home device. Further, this project could serve as the foundation for next step research and development.

Intern: 
Zihang Fu
Faculty Supervisor: 
Graeme Hirst
Province: 
Ontario
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