no code implementations • NAACL (ACL) 2022 • Pragaash Ponnusamy, Clint Solomon Mathialagan, Gustavo Aguilar, Chengyuan Ma, Chenlei Guo
Self-learning paradigms in large-scale conversational AI agents tend to leverage user feedback in bridging between what they say and what they mean.
no code implementations • RepL4NLP (ACL) 2022 • Md Mofijul Islam, Gustavo Aguilar, Pragaash Ponnusamy, Clint Solomon Mathialagan, Chengyuan Ma, Chenlei Guo
Additionally, the dependency on a fixed vocabulary limits the subword models' adaptability across languages and domains.
no code implementations • 9 Nov 2020 • Alireza Roshan-Ghias, Clint Solomon Mathialagan, Pragaash Ponnusamy, Lambert Mathias, Chenlei Guo
Spoken language understanding (SLU) systems in conversational AI agents often experience errors in the form of misrecognitions by automatic speech recognition (ASR) or semantic gaps in natural language understanding (NLU).
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
no code implementations • 12 Jun 2015 • Harsh Agrawal, Clint Solomon Mathialagan, Yash Goyal, Neelima Chavali, Prakriti Banik, Akrit Mohapatra, Ahmed Osman, Dhruv Batra
We are witnessing a proliferation of massive visual data.
no code implementations • CVPR 2015 • Clint Solomon Mathialagan, Andrew C. Gallagher, Dhruv Batra
We address two specific questions -- Given an image, who are the most important individuals in it?