no code implementations • insights (ACL) 2022 • Hyounghun Kim, Aishwarya Padmakumar, Di Jin, Mohit Bansal, Dilek Hakkani-Tur
Natural language guided embodied task completion is a challenging problem since it requires understanding natural language instructions, aligning them with egocentric visual observations, and choosing appropriate actions to execute in the environment to produce desired changes.
no code implementations • 11 Oct 2021 • Sashank Santhanam, Behnam Hedayatnia, Spandana Gella, Aishwarya Padmakumar, Seokhwan Kim, Yang Liu, Dilek Hakkani-Tur
We demonstrate the benefit of our Conv-FEVER dataset by showing that the models trained on this data perform reasonably well to detect factually inconsistent responses with respect to the provided knowledge through evaluation on our human annotated data.
1 code implementation • 1 Oct 2021 • Aishwarya Padmakumar, Jesse Thomason, Ayush Shrivastava, Patrick Lange, Anjali Narayan-Chen, Spandana Gella, Robinson Piramuthu, Gokhan Tur, Dilek Hakkani-Tur
Robots operating in human spaces must be able to engage in natural language interaction with people, both understanding and executing instructions, and using conversation to resolve ambiguity and recover from mistakes.
no code implementations • SIGDIAL (ACL) 2021 • Alexandros Papangelis, Karthik Gopalakrishnan, Aishwarya Padmakumar, Seokhwan Kim, Gokhan Tur, Dilek Hakkani-Tur
We show an average improvement of 35% in intent detection and 21% in slot tagging over a baseline model trained from the seed data.
no code implementations • 26 Jun 2020 • Aishwarya Padmakumar, Raymond J. Mooney
Dialog systems research has primarily been focused around two main types of applications - task-oriented dialog systems that learn to use clarification to aid in understanding a goal, and open-ended dialog systems that are expected to carry out unconstrained "chit chat" conversations.
no code implementations • 9 Jun 2020 • Aishwarya Padmakumar, Raymond J. Mooney
Intelligent systems need to be able to recover from mistakes, resolve uncertainty, and adapt to novel concepts not seen during training.
1 code implementation • 1 Mar 2019 • Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov, Nick Walker, Yuqian Jiang, Harel Yedidsion, Justin Hart, Peter Stone, Raymond J. Mooney
Natural language understanding for robotics can require substantial domain- and platform-specific engineering.
no code implementations • EMNLP 2018 • Aishwarya Padmakumar, Peter Stone, Raymond J. Mooney
Active learning identifies data points to label that are expected to be the most useful in improving a supervised model.
no code implementations • EACL 2017 • Aishwarya Padmakumar, Jesse Thomason, Raymond J. Mooney
Natural language understanding and dialog management are two integral components of interactive dialog systems.