Tharindu Madusanka

student
Final year undergraduate who like to take a quantitative approach to things in life, and someone who is dabbling in Reinforcement learning, Natural Language Processing and Data analytics. Particularly interested in conducting research that has practical applications.
 
I am working on the project Dialogue policy optimization in low resource setting and Building Task-Oriented Conversational Agents in Low-Resource Settings.
Brief Description of Project

Project 01

We introduce a comprehensive task-oriented conversational agent in low resource settings utilizing a novel pipeline ensemble technique to enhance natural language understanding tasks. The experiments conducted shows that our pipeline ensembling approach outperforms individual pipelines in precision, recall, f1-score and accuracy in both intent classification and entity extraction tasks. Furthermore, we implemented a Reinforcement Learning based dialogue policy learner addressing the overfitting issue by proposing a novel approach for synthetic agenda generation by acknowledging the underlying probability distribution of the user agendas with a reward-based sampling method that prioritizes failed dialogue acts

 

Project 02

The dialogue policy optimization in task oriented conversational agents, employed in low resource setting, is an open research project. We have developed a novel approach for dialogue policy optimization using Reinforcement Learning. The methodology is based on Self-play and a novel sampling technique that prioritizes failed dialogues over successful ones.
 

Team members - Durashi Langappuli, Thisara Welmilla