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.

Input: Consider Input: In particular, we investigated attention to nouns, verbs, pronouns, subjects, objects, and negation words, and special BERT tokens across the tasks.

Output: What handcrafter features-of-interest are used?


Input: Consider Input: We compare our model with other strong competitive methods on the SQuAD leaderboard and TriviaQA leaderboard.

Output: What other solutions do they compare to?


Input: Consider Input: For the MT task, we use the WMT 2014 En $\leftrightarrow $ Fr parallel corpus. The dataset contains 36 million En $\rightarrow $ Fr sentence pairs. We swapped the source and target sentences to obtain parallel data for the Fr $\rightarrow $ En translation task. We use these two datasets (72 million sentence pairs) to train a single multilingual NMT model to learn both these translation directions simultaneously. 


Output: What data were they used to train the multilingual encoder?