Probabilistic Labeling for Efficient Referential Grounding based on Collaborative Discourse

Changsong Liu, Lanbo She, Rui Fang, Joyce Y. Chai

Abstract: When humans and artificial agents (e.g. robots) have mismatched perceptions of the shared environment, referential communication between them becomes difficult. To mediate perceptual differences, this paper presents a new approach using probabilistic labeling for referential grounding. This approach aims to integrate different types of evidence from the collaborative referential discourse into a unified scheme. Its probabilistic labeling procedure can generate multiple grounding hypotheses to facilitate follow-up dialogue. Our empirical results have shown the probabilistic labeling approach significantly outperforms a previous graphmatching approach for referential grounding.