In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

Q: Through our experiments, we make subtle points related to: (a) the performance of our features, (b) how our approach compares against human ability to detect drunk-texting, (c) most discriminative stylistic features, and (d) an error analysis that points to future work.
Question: Do the authors mention any confounds to their study?

A: No
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Q: E2E NLG challenge Dataset: The training set of the E2E challenge dataset which consists of 42K samples was partitioned into a 10K paired and 32K unpaired datasets by a random process. The Wikipedia Company Dataset: The Wikipedia company dataset presented in Section SECREF18 was filtered to contain only companies having abstracts of at least 7 words and at most 105 words. The dataset was then divided into: a training set (35K), a development set (4.3K) and a test set (4.3K). The training set was also partitioned in order to obtain the paired and unpaired datasets.
Question: What non-annotated datasets are considered?

A: E2E NLG challenge Dataset The Wikipedia Company Dataset
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Q: In this study, we experiment four different classification models including logistic regression (LR), recurrent neural network (RNN) BIBREF35, convolutional neural network (CNN) BIBREF36 and Google BERT BIBREF37.
Question: Which document classifiers do they experiment with?

A:
logistic regression (LR), recurrent neural network (RNN) BIBREF35, convolutional neural network (CNN) BIBREF36 and Google BERT BIBREF37
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