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.

Ex Input:
As a document classifier we employ a word-based CNN similar to Kim consisting of the following sequence of layers: $ \texttt {Conv} \xrightarrow{} \texttt {ReLU} \xrightarrow{} \texttt {1-Max-Pool} \xrightarrow{} \texttt {FC} \\ $ Future work would include applying LRP to other neural network architectures (e.g. character-based or recurrent models) on further NLP tasks, as well as exploring how relevance information could be taken into account to improve the classifier's training procedure or prediction performance.

Ex Output:
Do the experiments explore how various architectures and layers contribute towards certain decisions?


Ex Input:
To simplify the task, we set up a binary classification: candidates who have been liked or shortlisted are considered part of the hirable class and others part of the not hirable class.

Ex Output:
How is "hirability" defined?


Ex Input:
To simulate the scenario, we split the Microsoft COCO dataset into food and non-food domain datasets. 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. Adaptation to food domain captioning

Ex Output:

How many examples are there in the source domain?