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.

Example Input: We evaluated our models on 3 different datasets:

CSAT dataset for CSAT prediction, consisting of spoken transcripts (automatic via ASR).

20 newsgroups for topic identification task, consisting of written text;

Fisher Phase 1 corpus for topic identification task, consisting of spoken transcripts (manual); 
 Question: What datasets did they use for evaluation?
Example Output: CSAT dataset 20 newsgroups Fisher Phase 1 corpus

Example 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?
Example Output: system combination on the decoding lattice level combination weights

Example Input:  This baseline has an F1 of 41.59% and accuracy of 71.22%. 
 Question: what are their evaluation metrics?
Example Output:
F1 accuracy