instruction:
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.
question:
We design the word-level refine decoder because this process is similar to the cloze task in BERT's pre-train process, therefore by using the ability of the contextual language model the decoder can generate more fluent and natural sequences.
answer:
Why masking words in the decoder is helpful?


question:
Annotator votes for one of the seven emotions, namely Ekman’s six basic emotions BIBREF1, plus the neutral.
answer:
What labels does the dataset have?


question:
Training was accomplished using the Kaldi Babel receipe using 198 hours of data in 10 languages (Bulgarian, Czech, French, German, Korean, Polish, Portuguese, Russian, Thai, Vietnamese) from GlobalPhone.
answer:

What languages are considered?