instruction:
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.
question:
We map each relation type $R(x,y)$ to at least one parametrized natural-language question $q_x$ whose answer is $y$ .
answer:
How is the input triple translated to a slot-filling task?


question:
We employ a huge dataset of 45,000 self-reported depressed users introduced in BIBREF46 where a lexicon of depression symptoms consisting of 1500 depression-indicative terms was created with the help of psychologist clinician and employed for collecting self-declared depressed individual's profiles. A subset of 8,770 users (24 million time-stamped tweets) containing 3981 depressed and 4789 control users (that do not show any depressive behavior) were verified by two human judges BIBREF46 .
answer:
How is the data annotated?


question:
We evaluate our model on both English and Chinese segmentation
answer:

What language do they look at?