instruction:
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.
question:
DNN based models coupled with transfer learning beat the best-known results for all three datasets. Previous best F1 scores for Wikipedia BIBREF4 and Twitter BIBREF8 datasets were 0.68 and 0.93 respectively. We achieve F1 scores of 0.94 for both these datasets using BLSTM with attention and feature level transfer learning (Table TABREF25 ). For Formspring dataset, authors have not reported F1 score. Their method has accuracy score of 78.5% BIBREF2 . We achieve F1 score of 0.95 with accuracy score of 98% for the same dataset.
answer:
What were their performance results?


question:
For each candidate mention span, we retrieve the top 10 entities according to the Freebase API. We then create a lattice in which the nodes correspond to mention-entity pairs, scored by their Freebase API scores, and the edges encode the fact that no joint assignment of entities to mentions can contain overlapping spans. We take the top 10 paths through the lattice as possible entity disambiguations. For each possibility, we generate $n$ -best paraphrases that contains the entity mention spans. In the end, this process creates a total of $10n$ paraphrases. 
answer:
How many paraphrases are generated per question?


question:
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.
answer:

What is the dataset used as input to the Word2Vec algorithm?