In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Q: CIC is a suitable complementary metric to ROUGE because it accounts for the most important information within each dialog domain. CIC can be applied to any summarization task with predefined essential entities.

A: Is new evaluation metric extension of ROGUE?
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Q: For the labeled data, we use audio from the IEMOCAP dataset, which comes with labels for activation and valence, both measured on a 5-point Likert scale from three distinct annotators.

A: Which multitask annotated corpus is used?
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Q: To establish a baseline, we consider a random method that predicts the positive label with 0.15 probability (the base rate of positive instances). Overall performance (Figure FIGREF28). Although our word-level task is heavily imbalanced, all of our models outperform the random baseline by a wide margin. As expected, content words are much more difficult to predict than stopwords, but the best F1 score in content words more than doubles that of the random baseline (0.286 vs. 0.116). Notably, although we strongly improve on our random baseline, even our best F1 scores are relatively low, and this holds true regardless of the model used. Despite involving more tokens than standard tagging tasks (e.g., BIBREF41 and BIBREF42), predicting whether a word is going to be echoed in explanations remains a challenging problem.

A:
What are overall baseline results on new this new task?
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