Grounded Misunderstandings in Asymmetric Dialogue: A Perspectivist
  Annotation Scheme for MapTask
Collaborative dialogue relies on participants incrementally establishing
common ground, yet in asymmetric settings they may believe they agree while
referring to different entities. We introduce a perspectivist annotation scheme
for the HCRC MapTask corpus (Anderson et al., 1991) that separately captures
speaker and addressee grounded interpretations for each reference expression,
enabling us to trace how understanding emerges, diverges, and repairs over
time. Using a scheme-constrained LLM annotation pipeline, we obtain 13k
annotated reference expressions with reliability estimates and analyze the
resulting understanding states. The results show that full misunderstandings
are rare once lexical variants are unified, but multiplicity discrepancies
systematically induce divergences, revealing how apparent grounding can mask
referential misalignment. Our framework provides both a resource and an
analytic lens for studying grounded misunderstanding and for evaluating
(V)LLMs' capacity to model perspective-dependent grounding in collaborative
dialogue.
690cf18592ee5ed33a0a0f84
Nan Li
claimed_verified
2025-11-07T10:31:14.866Z
61bf8017c88f3fd22f654086
/avatars/ea9e762b3db755fe051751577353fbca.svg
Nan Li
chnln
user
chnln
690cf18592ee5ed33a0a0f85
Albert Gatt
690cf18592ee5ed33a0a0f86
Massimo Poesio
Grounded Misunderstandings in Asymmetric Dialogue: A Perspectivist
  Annotation Scheme for MapTask
Collaborative dialogue relies on participants incrementally establishing
common ground, yet in asymmetric settings they may believe they agree while
referring to different entities. We introduce a perspectivist annotation scheme
for the HCRC MapTask corpus (Anderson et al., 1991) that separately captures
speaker and addressee grounded interpretations for each reference expression,
enabling us to trace how understanding emerges, diverges, and repairs over
time. Using a scheme-constrained LLM annotation pipeline, we obtain 13k
annotated reference expressions with reliability estimates and analyze the
resulting understanding states. The results show that full misunderstandings
are rare once lexical variants are unified, but multiplicity discrepancies
systematically induce divergences, revealing how apparent grounding can mask
referential misalignment. Our framework provides both a resource and an
analytic lens for studying grounded misunderstanding and for evaluating
(V)LLMs' capacity to model perspective-dependent grounding in collaborative
dialogue.
A perspectivist annotation scheme for the HCRC MapTask corpus reveals how understanding emerges, diverges, and repairs in collaborative dialogue, highlighting the role of multiplicity discrepancies in referential misalignment.
perspectivist annotation scheme HCRC MapTask corpus speaker and addressee grounded interpretations reference expression understanding states multiplicity discrepancies referential misalignment (V)LLMs perspective-dependent grounding