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.

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?
we obtain 52,053 dialogues and 460,358 utterances

In Table TABREF1, we summarize the quantitative results of the above previous studies. It includes the model basis, feature description, languages classified and the used dataset along with accuracy obtained. The table also lists the overall results of our proposed models (at the top). The languages used by various authors along with their acronyms are English (En), Spanish (Es), French (Fr), German (De), Russian (Ru), Italian (It), Bengali (Ben), Hindi (Hi) and Telegu (Tel).
Question: Is the performance compared against a baseline model?
Yes

We evaluate a multi-task approach and three algorithms that explicitly model the task dependencies.
Question: Will these findings be robust through different datasets and different question answering algorithms?
Yes