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.

Ex Input:
GPT-2 achieves the highest score and the $n$-gram the lowest. Transformer-XL and the LSTM LM perform in the middle, and at roughly the same level as each other. We report the 12-category accuracy results for all models and human evaluation in Table TABREF14.

Ex Output:
What is the performance of the models on the tasks?


Ex Input:
Sentiment Classification
We first conduct a multi-task experiment on sentiment classification.

We use 16 different datasets from several popular review corpora used in BIBREF20 . These datasets consist of 14 product review datasets and two movie review datasets.

All the datasets in each task are partitioned randomly into training set, development set and testing set with the proportion of 70%, 10% and 20% respectively. Transferability of Shared Sentence Representation
With attention mechanism, the shared sentence encoder in our proposed models can generate more generic task-invariant representations, which can be considered as off-the-shelf knowledge and then be used for unseen new tasks. Introducing Sequence Labeling as Auxiliary Task
A good sentence representation should include its linguistic information. Therefore, we incorporate sequence labeling task (such as POS Tagging and Chunking) as an auxiliary task into the multi-task learning framework, which is trained jointly with the primary tasks (the above 16 tasks of sentiment classification).

Ex Output:
What tasks did they experiment with?


Ex Input:
For each qualifying diagnosis tweet we retrieve the timeline of the corresponding Twitter user using the Twitter user_timeline API endpoint . Subsequently, we remove all non-English tweets (Twitter API machine-detected“lang” field), all retweets, and tweets that contain “diagnos*” or “depress*”, but not a valid diagnosis statement. The resulting Depressed cohort contains 1,207 individuals and 1,759,644 tweets ranging from from May 2008 to September 2018.

Ex Output:

Do they report results only on English datasets?