Human-robot interactions situated in a dynamic environment create a unique mix of challenges for conversational systems. We argue that, on the one hand, NLG can contribute to addressing these challenges and that, on the other hand, they pose interesting research problems for NLG. To illustrate our position we describe our research on non-humanoid robots using non-verbal signals to support communication.

We present the GIVE-2 Corpus, a new corpus of human instruction giving. The corpus was collected by asking one person in each pair of subjects to guide the other person towards completing a task in a virtual 3D environment with typed instructions. This is the same setting as that of the recent GIVE Challenge, and thus the corpus can serve as a source of data and as a point of comparison for NLG systems that participate in the GIVE Challenge. The instruction-giving data we collect is multilingual (45 German and 63 English dialogues), and can easily be extended to further languages by using our software, which we have made available. We analyze the corpus to study the effects of learning by repeated participation in the task and the effects of the participants' spatial navigation abilities. Finally, we present a novel annotation scheme for situated referring expressions and compare the referring expressions in the German and English data.