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.

Example 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).
Example Output: What tasks did they experiment with?

Example Input: The results in Table TABREF38 confirm the results of BIBREF13 and suggest that we successfully replicated a large proportion of their features. The results for all three prediction settings (one outgoing edge: INLINEFORM0 , support/attack: INLINEFORM1 and support/attack/neither: INLINEFORM2 ) across all type variables ( INLINEFORM3 , INLINEFORM4 and INLINEFORM5 ) are displayed in Table TABREF39 . All models significantly outperform the majority baseline with respect to macro F1.
Example Output: What baseline and classification systems are used in experiments?

Example Input: We used a dataset provided by Shortir, an Indonesian news aggregator and summarizer company. The dataset contains roughly 20K news articles. Each article has the title, category, source (e.g., CNN Indonesia, Kumparan), URL to the original article, and an abstractive summary which was created manually by a total of 2 native speakers of Indonesian
Example Output:
Did they use a crowdsourcing platform for the summaries?