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: We develop a null model to determine how much variation in binomial orderings we might expect across communities and across time, if binomial orderings were randomly ordered according to global asymmetry values.
Question: What new model is proposed for binomial lists?
Example Output: null model

Example Input: As previous systems collect relevant data from knowledge bases after observing questions during evaluation BIBREF24 , BIBREF25 , we also explore using this option. Namely, we build a customized text corpus based on questions in commonsense reasoning tasks.
Question: Do they fine-tune their model on the end task?
Example Output: Yes

Example Input: The dataset needed to train the W2V was obtained using the information extracted from a dump of the Italian Wikipedia (dated 2019.04.01), from the main categories of Italian Google News (WORLD, NATION, BUSINESS, TECHNOLOGY, ENTERTAINMENT, SPORTS, SCIENCE, HEALTH) and from some anonymized chats between users and a customer care chatbot (Laila). The dataset (composed of 2.6 GB of raw text) includes $421\,829\,960$ words divided into $17\,305\,401$ sentences. All the words were then transformed to lowercase (to avoid a double presence) finally producing a vocabulary of $618\,224$ words.
Question: What is the dataset used as input to the Word2Vec algorithm?
Example Output:
Italian Wikipedia and Google News extraction producing final vocabulary of 618224 words