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.

[Q]: 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.
Question: Why masking words in the decoder is helpful?
[A]: ecause 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.

[Q]: Overall, this resulted in a total of 34,432 user conversations. Together, these users gave Gunrock an average rating of 3.65 (median: 4.0), which was elicited at the end of the conversation ("On a scale from 1 to 5 stars, how do you feel about talking to this socialbot again?").
Question: What is the sample size of people used to measure user satisfaction?
[A]: 34,432 user conversations

[Q]: Twitter data: We used the Twitter API to scrap tweets with hashtags. All non-English tweets were filtered out by the API.
Question: Do they evaluate only on English datasets?
[A]:
Yes