Détail du document
Identifiant
oai:arXiv.org:2410.03959
Sujet
Computer Science - Computation and... Computer Science - Artificial Inte... Computer Science - Computer Vision... Computer Science - GraphicsAuteur
Tang, Zineng Mao, Lingjun Suhr, AlaneCatégorie
Computer Science
Année
2024
Date de référencement
09/10/2024
Mots clés
model paired comprehension science computerRésumé
We introduce a task and dataset for referring expression generation and comprehension in multi-agent embodied environments.
In this task, two agents in a shared scene must take into account one another's visual perspective, which may be different from their own, to both produce and understand references to objects in a scene and the spatial relations between them.
We collect a dataset of 2,970 human-written referring expressions, each paired with human comprehension judgments, and evaluate the performance of automated models as speakers and listeners paired with human partners, finding that model performance in both reference generation and comprehension lags behind that of pairs of human agents.
Finally, we experiment training an open-weight speaker model with evidence of communicative success when paired with a listener, resulting in an improvement from 58.9 to 69.3% in communicative success and even outperforming the strongest proprietary model.
;Comment: Accepted to EMNLP2024 Main
Tang, Zineng,Mao, Lingjun,Suhr, Alane, 2024, Grounding Language in Multi-Perspective Referential Communication
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