Recent work on dialogue-based collaborative plan acquisition (CPA) has
suggested that Theory of Mind (ToM) modelling can improve missing knowledge
prediction in settings with asymmetric skill-sets and knowledge. Although ToM
was claimed to be important for effective collaboration, its real impact on
this novel task remains under-explored. By representing plans as graphs and by
exploiting task-specific constraints we show that, as performance on CPA nearly
doubles when predicting one's own missing knowledge, the improvements due to
ToM modelling diminish. This phenomenon persists even when evaluating existing
baseline methods. To better understand the relevance of ToM for CPA, we report
a principled performance comparison of models with and without ToM features.
Results across different models and ablations consistently suggest that learned
ToM features are indeed more likely to reflect latent patterns in the data with
no perceivable link to ToM. This finding calls for a deeper understanding of
the role of ToM in CPA and beyond, as well as new methods for modelling and
evaluating mental states in computational collaborative agents.

通过将计划表示为图形并利用任务特定的约束条件，我们表明，在预测自己的缺失知识时，对心智模型的改进并没有显著提高协作计划获取的性能，这一现象甚至在评估现有基准方法时仍然存在。