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:
We obtained 9,892 stories of sexual harassment incidents that was reported on Safecity. Those stories include a text description, along with tags of the forms of harassment, e.g. commenting, ogling and groping. A dataset of these stories was published by Karlekar and Bansal karlekar2018safecity. In addition to the forms of harassment, we manually annotated each story with the key elements (i.e. “harasser", “time", “location", “trigger"), because they are essential to uncover the harassment patterns. An example is shown in Figure FIGREF3. Furthermore, we also assigned each story classification labels in five dimensions (Table TABREF4). The detailed definitions of classifications in all dimensions are explained below.

Ex Output:
What is the size of the dataset?


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:
At the same time, some information about inflected word forms in the context can be useful, but it is lost during lemmatization, and this leads to the decreased score. Arguably, this means that lemmatization brings along both advantages and disadvantages for WSD with ELMo. 

Ex Output:

Do the authors mention any downside of lemmatizing input before training ELMo?