In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Input: Consider Input: The final embedding dimensionality is equal to the number of unique word labels in the training set, which is 1061.

Output: Which dimensionality do they use for their embeddings?

Input: Consider Input: As the gold standard sentiment lexica, we chose manually created lexicon in Czech BIBREF11 , German BIBREF12 , French BIBREF13 , Macedonian BIBREF14 , and Spanish BIBREF15 .

Output: what sentiment sources do they compare with?

Input: Consider Input: Our baseline decoder is a standard beam search decoder BIBREF5 with several straightforward performance optimizations
Output: What baseline decoder do they use?