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.
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Question: 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.

Answer: What type of models are used for classification?


Question: Adaptation to food domain captioning Consequently, the food dataset has 3,806 images for training and 1,775 for validation. The non-food dataset has 78,976 images for training and 38,749 for validation.

Answer: How many examples are there in the target domain?


Question: We investigate the following six idiom resources:

Wiktionary;

the Oxford Dictionary of English Idioms (ODEI, BIBREF31);

UsingEnglish.com (UE);

the Sporleder corpus BIBREF10;

the VNC dataset BIBREF9;

and the SemEval-2013 Task 5 dataset BIBREF15.

Answer:
What dictionaries are used for automatic extraction of PIEs?