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.

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?
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

 Our street view environment was integrated into ParlAI BIBREF6 and used to collect a large-scale dataset on Mechanical Turk involving human perception, action and communication. 
 Question: What data did they use?
 dataset on Mechanical Turk involving human perception, action and communication

For the MT task, we use the WMT 2014 En $\leftrightarrow $ Fr parallel corpus. The dataset contains 36 million En $\rightarrow $ Fr sentence pairs. We swapped the source and target sentences to obtain parallel data for the Fr $\rightarrow $ En translation task. We use these two datasets (72 million sentence pairs) to train a single multilingual NMT model to learn both these translation directions simultaneously.  
 Question: What data were they used to train the multilingual encoder?

WMT 2014 En-Fr parallel corpus