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.
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Q: replacing this single GRU with two different components first component is a sentence reader second component is the input fusion layer
Question: What changes they did on input module?

A: For the DMN+, we propose replacing this single GRU with two different components. The first component is a sentence reader The second component is the input fusion layer
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Q: We mix standard linguistic features, such as Part-Of-Speech (POS) and chunk tag, together with several gazetteers specifically built for classical music, and a series of features representing tokens' left and right context.
Question: What kind of corpus-based features are taken into account?

A:
standard linguistic features, such as Part-Of-Speech (POS) and chunk tag series of features representing tokens' left and right context
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