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PhotoBook is a collaborative dialogue game where two players receive private,partially-overlapping sets of images and resolve which images they have incommon. It presents machines with a great challenge to learn how people buildcommon ground around multimodal context to communicate effectively. Methodsdeveloped in the literature, however, cannot be deployed to real gameplay sincethey only tackle some subtasks of the game, and they require additionalreference chains inputs, whose extraction process is imperfect. Therefore, wepropose a reference chain-free listener model that directly addresses thegame's predictive task, i.e., deciding whether an image is shared with partner.Our DeBERTa-based listener model reads the full dialogue, and utilizesCLIPScore features to assess utterance-image relevance. We achieve >77%accuracy on unseen sets of images/game themes, outperforming baseline by >17points.

Listener Model for the PhotoBook Referential Game with CLIPScores as Implicit Reference Chain