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: To learn better graph representation of multi-party dialogues, we adopt the dialogues with 8-15 utterances and 3-7 speakers. To simplify the task, we filter the dialogues with long sentences (more than 20 words). Finally, we obtain 52,053 dialogues and 460,358 utterances. 
 Question: How large is the proposed dataset?

A: we obtain 52,053 dialogues and 460,358 utterances
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Q: Fig. FIGREF4 shows the distributions of seven question types grouped deterministically from the lexicons. 
 Question: How many question types do they find in the datasets analyzed?

A: seven 
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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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