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.

Example Input: This step consists of generating a query out of the claim and querying a search engine (here, we experiment with Google and Bing) in order to retrieve supporting documents. Rather than querying the search engine with the full claim (as on average, a claim is two sentences long), we generate a shorter query following the lessons highlighted in BIBREF0 . We rank the words by means of tf-idf. We compute the idf values on a 2015 Wikipedia dump and the English Gigaword. BIBREF0 suggested that a good way to perform high-quality search is to only consider the verbs, the nouns and the adjectives in the claim; thus, we exclude all words in the claim that belong to other parts of speech. Moreover, claims often contain named entities (e.g., names of persons, locations, and organizations); hence, we augment the initial query with all the named entities from the claim's text. We use IBM's AlchemyAPI to identify named entities. Ultimately, we generate queries of 510 tokens, which we execute against a search engine. We then collect the snippets and the URLs in the results, skipping any result that points to a domain that is considered unreliable.
Question: How are the potentially relevant text fragments identified?
Example Output: Generate a query out of the claim and querying a search engine, rank the words by means of TF-IDF, use IBM's AlchemyAPI to identify named entities, generate queries of 510 tokens, which execute against a search engine, and collect the snippets and the URLs in the results, skipping any result that points to a domain that is considered unreliable.

Example Input: We use Gaussian Processes as this probabilistic kernelised framework avoids the need for expensive cross-validation for hyperparameter selection
Question: Why is a Gaussian process an especially appropriate method for this classification problem?
Example Output: avoids the need for expensive cross-validation for hyperparameter selection

Example Input: 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).
Question: What datasets are used for training/testing models?
Example Output:
Microsoft Research dataset containing movie, taxi and restaurant domains.