In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

Input: Consider Input: While the performances of a purely content-based model naturally stays stable, the performance of the other systems decrease notably they perform worse than the content-based model.
Question: How do they demonstrate the robustness of their results?

Output: performances of a purely content-based model naturally stays stable

Input: Consider Input: We evaluate our model in a simulated binning task in which the robot is tasked to place a cube into a bowl as outlined by the verbal command. Each environment contains between three and five objects differentiated by their size (small, large), shape (round, square) and color (red, green, blue, yellow, pink), totalling in 20 different objects. Depending on the generated scenario, combinations of these three features are necessary to distinguish the targets from each other, allowing for tasks of varying complexity.
Question: What simulations are performed by the authors to validate their approach?

Output: a simulated binning task in which the robot is tasked to place a cube into a bowl as outlined by the verbal command

Input: Consider Input: We use the Dialogue State Tracking Competition 2 (DSTC2) dataset BIBREF27 which is the most widely used dataset for research on task-oriented chatbots. We also used two other datasets recently open-sourced by Google Research BIBREF28 which are M2M-sim-M (dataset in movie domain) and M2M-sim-R (dataset in restaurant domain).
Question: What are the three datasets used?
Output: DSTC2 M2M-sim-M M2M-sim-R