In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

[Q]: The experiment dataset comes from Microsoft Research (MSR) . It contains three domains: movie, taxi, and restaurant. The total count of dialogues per domain and train/valid/test split is reported in Table TABREF11. At every turn both user and agent acts are annotated, we use only the agent side as targets in our experiment. The acts are ordered in the dataset (each output sentence aligns with one act).
[A]: What datasets are used for training/testing models?

[Q]: To evaluate model robustness, we devise a test set consisting of 'adversarial' examples, i.e, perturbed examples that can potentially change the base model's prediction. These could stem from paraphrasing a sentence, e.g., lexical and syntactical changes. We use two approaches described in literature: back-translation and noisy sequence autoencoder. Note that these examples resemble black-box attacks but are not intentionally designed to fool the system and hence, we use the term 'adversarial' broadly. We use these techniques to produce many paraphrases and find a subset of utterances that though very similar to the original test set, result in wrong predictions. We will measure the model robustness against such changes.
[A]: How authors create adversarial test set to measure model robustness?

[Q]: In this section we detail the discussions we use to test our metric and how we determine the ground truth (i.e. if the discussion is controversial or not). We use thirty different discussions that took place between March 2015 and June 2019, half of them with controversy and half without it. We considered discussions in four different languages: English, Portuguese, Spanish and French, occurring in five regions over the world: South and North America, Western Europe, Central and Southern Asia. We also studied these discussions taking first 140 characters and then 280 from each tweet to analyze the difference in performance and computing time wrt the length of the posts.

[A]:
How many languages do they experiment with?