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:
To answer RQ2, which is concerned with in-domain ATSC performance, we see in tab:results that for the in-domain training case, our models BERT-ADA Lapt and BERT-ADA Rest achieve performance close to state-of-the-art on the laptops dataset and new state-of-the-art on the restaurants dataset with accuracies of $79.19\%$ and $87.14\%$, respectively. In general, the ATSC task generalizes well cross-domain, with about 2-$3\%$ drop in accuracy compared to in-domain training.

Ex Output:
What are the performance results?

Ex Input:
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. We use CoNLL 2000 BIBREF22 sequence labeling dataset for both POS Tagging and Chunking tasks.

Ex Output:
What dataset did they use?

Ex Input:
Additionally, we also test their performance only on the items from the Zurich and Visp dialect, because most of the samples are from this two dialects. In Table TABREF15 we show the result of the comparison of the two models.

Ex Output:
Is the model evaluated on the graphemes-to-phonemes task?