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.

Q: Parallel Scan Inference
The commutative properties of semiring algorithms allow flexibility in the order in which we compute $A(\ell )$. Typical implementations of dynamic programming algorithms are serial in the length of the sequence. Vectorized Parsing
Computational complexity is even more of an issue for parsing algorithms, which cannot be as easily parallelized. Semiring Matrix Operations
The two previous optimizations reduce most of the cost to semiring matrix multiplication. In the specific case of the $(\sum , \times )$ semiring these can be computed very efficiently using matrix multiplication, which is highly-tuned on GPU hardware. Unfortunately for other semirings, such as log and max, these operations are either slow or very memory inefficient. For instance, for matrices $T$ and $U$ of sized $N \times M$ and $M \times O$, we can broadcast with $\otimes $ to a tensor of size $N \times M \times O$ and then reduce dim $M$ by $\bigoplus $ at a huge memory cost. 
 Question: What baselines are used in experiments?

A: Typical implementations of dynamic programming algorithms are serial in the length of the sequence Computational complexity is even more of an issue for parsing algorithms, which cannot be as easily parallelized Unfortunately for other semirings, such as log and max, these operations are either slow or very memory inefficient
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Q:  To evaluate real-world performance of our selected classifier (i.e., performance in the absence of model and parameter bias), we perform classification of the holdout set. On this set, our classifier had an accuracy and F1-score of 89.6% and 89.2%, respectively.  
 Question: What was their system's performance?

A: accuracy and F1-score of 89.6% and 89.2%, respectively
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Q: 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?

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