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.

Ex Input:
As before, the training and test sets contain some 30,000 and 5,000 sentence pairs, respectively 
 Question: How many samples did they generate for the artificial language?

Ex Output:
70,000


Ex Input:
To further improve the performance, we adopt system combination on the decoding lattice level. By combining systems, we can take advantage of the strength of each model that is optimized for different domains.  The best result for vlsp2018 of 4.85% WER is obtained by the combination weights 0.6:0.4 where 0.6 is given to the general language model and 0.4 is given to the conversation one. On the vlsp2019 set, the ratio is change slightly by 0.7:0.3 to deliver the best result of 15.09%. 
 Question: What is the language model combination technique used in the paper?

Ex Output:
system combination on the decoding lattice level combination weights


Ex Input:
Language Model (LM): We train a two layer recurrent neural language model with GRU cells of hidden size 512.

 Sequence-to-Sequence Attention Model (S2S): We train a two layer neural sequence to sequence model equipped with bi-linear attention function with GRU cells of hidden size 512.  Linear Dynamical System (LDS): We also train a linear dynamical system as discussed in Section SECREF1 as one of our baselines for fair comparisons. Semi-Supervised SLDS (SLDS-X%): To gauge the usability of semi-supervision, we also train semi-supervised SLDS models with varying amount of labelled sentiment tags unlike the original model which uses 100% tagged data.  
 Question: What baselines are used?

Ex Output:

a two layer recurrent neural language model with GRU cells of hidden size 512 a two layer neural sequence to sequence model equipped with bi-linear attention function with GRU cells of hidden size 512 a linear dynamical system semi-supervised SLDS models with varying amount of labelled sentiment tags