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: As a baseline for the RNN models, we apply a uni-directional RNN which predicts the relation after processing the whole sentence.
Question: Which variant of the recurrent neural network do they use?

A: uni-directional RNN
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Q: Probing Methodology and Modeling ::: Task Definition and Modeling ::: Baselines and Sanity Checks.
When creating synthetic datasets, it is important to ensure that systematic biases, or annotation artifacts BIBREF41, are not introduced into the resulting probes and that the target datasets are sufficiently challenging (or good, in the sense of BIBREF42). To test for this, we use several of the MCQA baseline models first introduced in BIBREF0, which take inspiration from the LSTM-based models used in BIBREF43 for NLI and various partial-input baselines based on these models.
Question: How do they control for annotation artificats?

A: we use several of the MCQA baseline models first introduced in BIBREF0
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Q: As it is reported that conservatives and liberals exhibit different behaviors on online social platforms BIBREF19BIBREF20BIBREF21, we further assigned a political bias label to different US outlets (and therefore news articles) following the procedure described in BIBREF2. In order to assess the robustness of our method, we performed classification experiments by training only on left-biased (or right-biased) outlets of both disinformation and mainstream domains and testing on the entire set of sources, as well as excluding particular sources that outweigh the others in terms of samples to avoid over-fitting.
Question: How is the political bias of different sources included in the model?

A:
By assigning a political bias label to each news article and training only on left-biased or right-biased outlets of both disinformation and mainstream domains
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