Search Results for author: Aishwarya Padmakumar

Found 9 papers, 2 papers with code

On the Limits of Evaluating Embodied Agent Model Generalization Using Validation Sets

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.

Rome was built in 1776: A Case Study on Factual Correctness in Knowledge-Grounded Response Generation

no code implementations11 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.

Response Generation

TEACh: Task-driven Embodied Agents that Chat

1 code implementation1 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.

Dialogue Understanding

Dialog as a Vehicle for Lifelong Learning

no code implementations26 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.

Dialog Policy Learning for Joint Clarification and Active Learning Queries

no code implementations9 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.

Active Learning Image Retrieval

Learning a Policy for Opportunistic Active Learning

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.

Active Learning reinforcement-learning

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