activity
20172022
most citedAre We There Yet? Learning to Localize in Embodied Instruction Following

7 citations · 15 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CL2022

Towards Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue Systems

Yi-Lin Tuan, Sajjad Beygi, Maryam Fazel-Zarandi +3

Users interacting with voice assistants today need to phrase their requests in a very specific manner to elicit an appropriate response. This limits the user experience, and is par…

cs.CV20215 cited

Embodied BERT: A Transformer Model for Embodied, Language-guided Visual Task Completion

Alessandro Suglia, Qiaozi Gao, Jesse Thomason +2

Language-guided robots performing home and office tasks must navigate in and interact with the world. Grounding language instructions against visual observations and actions to tak…

cs.AI20217 cited

Are We There Yet? Learning to Localize in Embodied Instruction Following

Shane Storks, Qiaozi Gao, Govind Thattai +1

Embodied instruction following is a challenging problem requiring an agent to infer a sequence of primitive actions to achieve a goal environment state from complex language and vi…

cs.CL20203 cited

Interactive Teaching for Conversational AI

Qing Ping, Feiyang Niu, Govind Thattai +7

Current conversational AI systems aim to understand a set of pre-designed requests and execute related actions, which limits them to evolve naturally and adapt based on human inter…

cs.CL2019

Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches

Shane Storks, Qiaozi Gao, Joyce Y. Chai

In the NLP community, recent years have seen a surge of research activities that address machines' ability to perform deep language understanding which goes beyond what is explicit…

cs.AI2017

Interactive Learning of State Representation through Natural Language Instruction and Explanation

Qiaozi Gao, Lanbo She, Joyce Y. Chai

One significant simplification in most previous work on robot learning is the closed-world assumption where the robot is assumed to know ahead of time a complete set of predicates…