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.

[EX Q]: First we are comparing BiLSTMBIBREF14, BiLSTM+CNNBIBREF20, BiLSTM+CRFBIBREF1, BiLSTM+CNN+CRFBIBREF2 models with CNN modelBIBREF0 and Stanford CRF modelBIBREF21.
Question: What is the baseline?
[EX A]: CNN modelBIBREF0 Stanford CRF modelBIBREF21

[EX 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?
[EX A]: No

[EX Q]: By choosing English (En) as the pivot language, we perform pivot alignments for identical English segments on Europarl Fr-En and En-De parallel corpora BIBREF18 , constructing a multi-parallel corpus of Fr-En-De. Then each of the Fr*-De and Fr-De* pseudo parallel corpora is established from the multi-parallel data by applying the pivot language-based translation described in the previous section. For automatic translation, we utilize a pre-trained and publicly released NMT model for En $
ightarrow $ De and train another NMT model for En $
ightarrow $ Fr using the WMT'15 En-Fr parallel corpus BIBREF19 .
Question: Where do they collect the synthetic data?
[EX A]:
Yes