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: The first type of classifier we evaluate in this work are feedforward neural networks (DNNs) consisting of 3 hidden layers, each with 512 rectified linear units (ReLUs) with a softmax activation function. As a second classifier to evaluate, we use convolutional neural networks (CNNs) with 2 convolutional and max pooling layers, followed by 2 fully-connected ReLU layers with 512 nodes.

A: What type of models are used for classification?
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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: We are using the disaster data from BIBREF5. It contains various dataset including the CrisiLexT6 dataset which contains six crisis events related to English tweets in 2012 and 2013, labeled by relatedness (on-topic and off-topic) of respective crisis. Each crisis event tweets contain almost 10,000 labeled tweets but we are only focused on flood-related tweets thus, we experimented with only two flood event i.e. Queensland flood in Queensland, Australia and Alberta flood in Alberta, Canada and relabeled all on-topic tweets as Related and Off-topic as Unrelated for implicit class labels understanding in this case.

A:
What dataset did they use?
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