Felix Gervits


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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It’s About Time: Turn-Entry Timing For Situated Human-Robot Dialogue
Felix Gervits | Ravenna Thielstrom | Antonio Roque | Matthias Scheutz
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

Turn-entry timing is an important requirement for conversation, and one that spoken dialogue systems largely fail at. In this paper, we introduce a computational framework based on work from Psycholinguistics, which is aimed at achieving proper turn-taking timing for situated agents. The approach involves incremental processing and lexical prediction of the turn in progress, which allows a situated dialogue system to start its turn and initiate actions earlier than would otherwise be possible. We evaluate the framework by integrating it within a cognitive robotic architecture and testing performance on a corpus of task-oriented human-robot directives. We demonstrate that: 1) the system is superior to a non-incremental system in terms of faster responses, reduced gap between turns, and the ability to perform actions early, 2) the system can time its turn to come in immediately at a transition point or earlier to produce several types of overlap, and 3) the system is robust to various forms of disfluency in the input. Overall, this domain-independent framework can be integrated into various dialogue systems to improve responsiveness, and is a step toward more natural, human-like turn-taking behavior.

2018

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ScoutBot: A Dialogue System for Collaborative Navigation
Stephanie M. Lukin | Felix Gervits | Cory J. Hayes | Pooja Moolchandani | Anton Leuski | John G. Rogers III | Carlos Sanchez Amaro | Matthew Marge | Clare R. Voss | David Traum
Proceedings of ACL 2018, System Demonstrations

ScoutBot is a dialogue interface to physical and simulated robots that supports collaborative exploration of environments. The demonstration will allow users to issue unconstrained spoken language commands to ScoutBot. ScoutBot will prompt for clarification if the user’s instruction needs additional input. It is trained on human-robot dialogue collected from Wizard-of-Oz experiments, where robot responses were initiated by a human wizard in previous interactions. The demonstration will show a simulated ground robot (Clearpath Jackal) in a simulated environment supported by ROS (Robot Operating System).

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Dialogue Structure Annotation for Multi-Floor Interaction
David Traum | Cassidy Henry | Stephanie Lukin | Ron Artstein | Felix Gervits | Kimberly Pollard | Claire Bonial | Su Lei | Clare Voss | Matthew Marge | Cory Hayes | Susan Hill
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Towards a Conversation-Analytic Taxonomy of Speech Overlap
Felix Gervits | Matthias Scheutz
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Pardon the Interruption: Managing Turn-Taking through Overlap Resolution in Embodied Artificial Agents
Felix Gervits | Matthias Scheutz
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

Speech overlap is a common phenomenon in natural conversation and in task-oriented interactions. As human-robot interaction (HRI) becomes more sophisticated, the need to effectively manage turn-taking and resolve overlap becomes more important. In this paper, we introduce a computational model for speech overlap resolution in embodied artificial agents. The model identifies when overlap has occurred and uses timing information, dialogue history, and the agent’s goals to generate context-appropriate behavior. We implement this model in a Nao robot using the DIARC cognitive robotic architecture. The model is evaluated on a corpus of task-oriented human dialogue, and we find that the robot can replicate many of the most common overlap resolution behaviors found in the human data.

2016

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Disfluent but effective? A quantitative study of disfluencies and conversational moves in team discourse
Felix Gervits | Kathleen Eberhard | Matthias Scheutz
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Situated dialogue systems that interact with humans as part of a team (e.g., robot teammates) need to be able to use information from communication channels to gauge the coordination level and effectiveness of the team. Currently, the feasibility of this end goal is limited by several gaps in both the empirical and computational literature. The purpose of this paper is to address those gaps in the following ways: (1) investigate which properties of task-oriented discourse correspond with effective performance in human teams, and (2) discuss how and to what extent these properties can be utilized in spoken dialogue systems. To this end, we analyzed natural language data from a unique corpus of spontaneous, task-oriented dialogue (CReST corpus), which was annotated for disfluencies and conversational moves. We found that effective teams made more self-repair disfluencies and used specific communication strategies to facilitate grounding and coordination. Our results indicate that truly robust and natural dialogue systems will need to interpret highly disfluent utterances and also utilize specific collaborative mechanisms to facilitate grounding. These data shed light on effective communication in performance scenarios and directly inform the development of robust dialogue systems for situated artificial agents.