Gordon Briggs


2021

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How Should Agents Ask Questions For Situated Learning? An Annotated Dialogue Corpus
Felix Gervits | Antonio Roque | Gordon Briggs | Matthias Scheutz | Matthew Marge
Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue

Intelligent agents that are confronted with novel concepts in situated environments will need to ask their human teammates questions to learn about the physical world. To better understand this problem, we need data about asking questions in situated task-based interactions. To this end, we present the Human-Robot Dialogue Learning (HuRDL) Corpus - a novel dialogue corpus collected in an online interactive virtual environment in which human participants play the role of a robot performing a collaborative tool-organization task. We describe the corpus data and a corresponding annotation scheme to offer insight into the form and content of questions that humans ask to facilitate learning in a situated environment. We provide the corpus as an empirically-grounded resource for improving question generation in situated intelligent agents.

2020

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Generating Quantified Referring Expressions through Attention-Driven Incremental Perception
Gordon Briggs
Proceedings of the 13th International Conference on Natural Language Generation

We model the production of quantified referring expressions (QREs) that identity collections of visual items. A previous approach, called Perceptual Cost Pruning, modeled human QRE production using a preference-based referring expression generation algorithm, first removing facts from the input knowledge base based on a model of perceptual cost. In this paper, we present an alternative model that incrementally constructs a symbolic knowledge base through simulating human visual attention/perception from raw images. We demonstrate that this model produces the same output as Perceptual Cost Pruning. We argue that this is a more extensible approach and a step toward developing a wider range of process-level models of human visual description.

2019

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Generating Quantified Referring Expressions with Perceptual Cost Pruning
Gordon Briggs | Hillary Harner
Proceedings of the 12th International Conference on Natural Language Generation

We model the production of quantified referring expressions (QREs) that identify collections of visual items. To address this task, we propose a method of perceptual cost pruning, which consists of two steps: (1) determine what subset of quantity information can be perceived given a time limit t, and (2) apply a preference order based REG algorithm (e.g., the Incremental Algorithm) to this reduced set of information. We demonstrate that this method successfully improves the human-likeness of the IA in the QRE generation task and successfully models human-generated language in most cases.

2014

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Modeling Blame to Avoid Positive Face Threats in Natural Language Generation
Gordon Briggs | Matthias Scheutz
Proceedings of the INLG and SIGDIAL 2014 Joint Session

2011

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Facilitating Mental Modeling in Collaborative Human-Robot Interaction through Adverbial Cues
Gordon Briggs | Matthias Scheutz
Proceedings of the SIGDIAL 2011 Conference