Rishabh Joshi

Language Technologies InstituteSchool of Computer ScienceCarnegie Mellon University
Gates Hillman Center, 5000 Forbes Ave, Pittsburgh, PA 15213

I am a second year MLT (Master's in Language Technologies) student in the Language Technologies Institute, School of Computer Science at Carnegie Mellon University, co-advised by Alan W Black, Yulia Tsvetkov and Alex Rudnicky. I'm also a member of Tsvetshop.

My research focuses on developing algorithms for machine learning and applying my methods in order to study how machines can mimic humans in congitive tasks, especially those related to Language. My research with Alan and Yulia focused on negotiation and persuasion in dialogue systems where we are trying to understand the qualities of good/bad sellers/persuaders and including explicit strategy sequence information to build a system that can negotiate better. With Alex, I worked with CMU's team, Tartan, on the Amazon Alexa Challenge, 2020 and we managed to reach the semi-finals. I also studyed coherence in dialogue and the right questions to ask for better engagement, by using external knowledge to augment the models.

Right now, my focus is on low resource NLP and exploring knowledge proliferation. We are exploring adapter based approaches for multilingual summarization. I'm also working on understanding and profiling important concepts in academic publications, which can later be used to study evolution of science.

Before I joined CMU, I worked in Samsung Research Institute, Bangalore, India where I was working in the Voice Intelligence Team on dialogue systems and chat-bots. Before that, I spent a wonderful semester working with Partha Talukdar at Machine and Language Learning (MALL) Lab in the Indian Institute of Science, Bangalore. I graduated with a B.Eng. in Computer Science from Birla Institute of Technology and Science, Pilani, India. In my Bachelor's thesis, I worked on incorporating external knowledge in distantly supervised neural relation extraction methods as part of iNELL (which is based on NELL).


Jul 4, 2021

Our work on Improving Broad-Coverage Medical Entity Linking with Semantic Type Prediction and Large-Scale Datasets was accepted at the Journal of Biomedical Informatics.

Jan 13, 2021

Our work on DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues. was accepted at the International Conference on Learning Representations (ICLR) 2021.

Jan 3, 2021

Our work on ResPer: Computationally Modelling Resisting Strategies in Persuasive Conversations was accepted at the European Association for Computational Linguistics (EACL) 2021.

Dec 3, 2020

Our work on MedType: Improving Medical Entity Linking with Semantic Type Prediction was accepted at the AMIA 2021 Virtual Informatics Summit.

Sep 21, 2020

Our work on Keeping Up Appearances: Computational Modeling of Face Acts in Persuasion Oriented Discussions was accepted at the 2020 Conference on Empirical Methods in Natural Language Processing.

Jul 1, 2020

Our work on Incorporating Multi-Level Features for Multi-Granular Propaganda Span Identification was accepted at the 14th International Workshop on Semantic Evaluation, 2020.

Jun 1, 2020

Our team, Tartan, reached the semi-finals of the 3rd Alexa Dialogue Challenge, 2019.

Mar 16, 2020

Our work on Analyzing the Extent of Misinformation in Cancer Related Tweets was accepted for poster presentation at ICWSM 2020.

Feb 12, 2020

Our work on A Multistream Vector Representation for use in Natural Dialog was accepted for poster presentation at LREC 2020.

Aug 22, 2019

Gave a talk at the 2019 LTI Student Research Symposium. Won a honourable mention award.