In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

We used a dataset provided by Shortir, an Indonesian news aggregator and summarizer company. The dataset contains roughly 20K news articles. Each article has the title, category, source (e.g., CNN Indonesia, Kumparan), URL to the original article, and an abstractive summary which was created manually by a total of 2 native speakers of Indonesian 
 Question: Did they use a crowdsourcing platform for the summaries?
No

To fine-tune BERT, we used a proprietary corpus that consists of hundreds of thousands of legal agreements. 
 Question: How big is dataset used for fine-tuning BERT?
hundreds of thousands of legal agreements

Our AV in-cabin data-set includes 30 hours of multimodal data collected from 30 passengers (15 female, 15 male) in 20 rides/sessions. 10 types of passenger intents are identified and annotated as: Set/Change Destination, Set/Change Route (including turn-by-turn instructions), Go Faster, Go Slower, Stop, Park, Pull Over, Drop Off, Open Door, and Other (turn music/radio on/off, open/close window/trunk, change AC/temp, show map, etc.). Relevant slots are identified and annotated as: Location, Position/Direction, Object, Time-Guidance, Person, Gesture/Gaze (this, that, over there, etc.), and None. In addition to utterance-level intent types and their slots, word-level intent keywords are annotated as Intent as well.  
 Question: What intents does the paper explore?

Set/Change Destination Set/Change Route Go Faster Go Slower Stop Park Pull Over Drop Off Open Door Other