activity
20162022
most citedApplying the Wizard-of-Oz Technique to Multimodal Human-Robot Dialogue

26 citations · 60 across the 6 of their papers we have counts for

collaborators

14 papers

cs.CL2022

Language Model Pre-Training with Sparse Latent Typing

Liliang Ren, Zixuan Zhang, Han Wang +3

Modern large-scale Pre-trained Language Models (PLMs) have achieved tremendous success on a wide range of downstream tasks. However, most of the LM pre-training objectives only foc…

cs.RO20191 cited

A Research Platform for Multi-Robot Dialogue with Humans

Matthew Marge, Stephen Nogar, Cory J. Hayes +4

This paper presents a research platform that supports spoken dialogue interaction with multiple robots. The demonstration showcases our crafted MultiBot testing scenario in which u…

cs.CL2019

Visual Understanding and Narration: A Deeper Understanding and Explanation of Visual Scenes

Stephanie M. Lukin, Claire Bonial, Clare R. Voss

We describe the task of Visual Understanding and Narration, in which a robot (or agent) generates text for the images that it collects when navigating its environment, by answering…

cs.RO2018

Balancing Efficiency and Coverage in Human-Robot Dialogue Collection

Matthew Marge, Claire Bonial, Stephanie Lukin +11

We describe a multi-phased Wizard-of-Oz approach to collecting human-robot dialogue in a collaborative search and navigation task. The data is being used to train an initial automa…

cs.CL2018

A Pipeline for Creative Visual Storytelling

Stephanie M. Lukin, Reginald Hobbs, Clare R. Voss

Computational visual storytelling produces a textual description of events and interpretations depicted in a sequence of images. These texts are made possible by advances and cross…

cs.CL2018

Consequences and Factors of Stylistic Differences in Human-Robot Dialogue

Stephanie M. Lukin, Kimberly A. Pollard, Claire Bonial +5

This paper identifies stylistic differences in instruction-giving observed in a corpus of human-robot dialogue. Differences in verbosity and structure (i.e., single-intent vs. mult…