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.
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Question: 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. We obtained 1260 unique utterances having commands to AMIE from our in-cabin data-set. We expanded this data-set via Amazon Mechanical Turk and ended up with 3347 utterances having intents. The annotations for intents and slots are obtained on the transcribed utterances by majority voting of 3 annotators. 
 Question: What are the supported natural commands?

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


Question: The third person plural pronoun `they' has no gender in English and most other languages (unlike the third person singular pronouns `he', `she', and `it').  If these sentences are translated into French, then `they' in the first sentence should be translated `elles', as referring to Jane and Susan, and `they' in the second sentence should be translated `ils', as referring to Fred and George. A couple of examples: The word `sie' in German serves as both the formal second person prounoun (always capitalized), the third person feminine singular, and the third person plural.  
 Question: What language do they explore?

Answer: English French German 


Question: INLINEFORM0 in the subsampled BIBREF2 training corpus and incrementing cell INLINEFORM1 for every context word INLINEFORM2 appearing within this window (forming a INLINEFORM3 pair). LexVec adjusts the PPMI matrix using context distribution smoothing BIBREF3 . We compare 1) the use of n-gram subwords, like fastText, and 2) unsupervised morphemes identified using Morfessor BIBREF11 to learn whether more linguistically motivated subwords offer any advantage over simple n-grams. 
 Question: What types of subwords do they incorporate in their model?

Answer:
n-gram subwords unsupervised morphemes identified using Morfessor BIBREF11 to learn whether more linguistically motivated subwords