Collaborative Effort towards Common Ground in Situated Human-Robot Dialogue

By Joyce Yue Chai, Lanbo She, Rui Fang, Spencer Ottarson, Cody Littley, Changsong Liu, K. Hanson. 2014

106 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Social Psychology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Joyce Yue Chai
2. Lanbo She
3. Changsong Liu
4. Rui Fang
5. Qiaozi Gao
6. Shaohua Yang
7. Sky CH-Wang
8. Leah Perlmutter

In Social Psychology

1. Joyce Yue Chai
2. Changsong Liu
3. Maya Cakmak

Citations in the corpus

• Language to Action: Towards Interactive Task Learning with Physical Agents (2018, result): “As shown in our previous work [Chai et al. , 2014], enabling transparency from the agent about its internal representations can reveal misunderstanding and significantly improve common ground.”
• Visual Referring Expression Recognition: What Do Systems Actually Learn? (2018, unknown): “Referring expression recognition and generation is a well studied problem in intelligent user interfaces (Chai et al., 2004), human-robot interaction (Fang et al., 2012; Chai et al., 2014; Williams et al., 2016), and situated dialogue (Kennington and Schlangen, 2017).”
• Back to the Blocks World: Learning New Actions through Situated Human-Robot Dialogue (2014, background): “Previous work on mediating perceptual differences between humans and robots have shown that a high collaborative effort from the robot leads to better referential grounding (Chai et al., 2014).”
• Collaborative Language Grounding Toward Situated Human-Robot Dialogue (2017, methodology): “The first experiment investigates the role of collaborative effort from the robot in facilitating language interpretation to establish a common ground of shared environment (Chai et al. 2014).”
• Learning to Mediate Perceptual Differences in Situated Human-Robot Dialogue (2015, methodology): “More specifically, we first used the same language and vision processing system as in the data-collecting experiment (see (Chai et al. 2014) for more details) to build two graph representations, i.e., a language graph representing all the human subject’s referring expressions and a vision graph…”
• Situated Language Understanding with Human-like and Visualization-Based Transparency (2016, background): “Others propose to use dialogue to collaboratively and incrementally move towards a common ground about a reference [1, 2, 5, 7, 14, 18].”
• MindCraft: Theory of Mind Modeling for Situated Dialogue in Collaborative Tasks (2021, background): “…MindCraft including in custom 2D worlds (Liu et al., 2012; Udagawa and Aizawa, 2020, 2019), in the physical world with human-robot interactions (McGuire et al., 2002; Chai et al., 2014, 2018; Thomason et al., 2020), and in various 3D virtual worlds (Bisk et al., 2018; Suhr et al., 2019).”