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Embodied dialogue instruction following requires an agent to complete acomplex sequence of tasks from a natural language exchange. The recentintroduction of benchmarks (Padmakumar et al., 2022) raises the question of howbest to train and evaluate models for this multi-turn, multi-agent,long-horizon task. This paper contributes to that conversation, by arguing thatimitation learning (IL) and related low-level metrics are actually misleadingand do not align with the goals of embodied dialogue research and may hinderprogress. We provide empirical comparisons of metrics, analysis of threemodels, and make suggestions for how the field might best progress. First, weobserve that models trained with IL take spurious actions during evaluation.Second, we find that existing models fail to ground query utterances, which areessential for task completion. Third, we argue evaluation should focus onhigher-level semantic goals.

Don't Copy the Teacher: Data and Model Challenges in Embodied Dialogue