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: Several recent works have investigated jointly training the acoustic model with a masking speech enhancement model BIBREF11, BIBREF12, BIBREF13, but these works did not evaluate their system on speech enhancement metrics. Indeed, our internal experiments show that without access to the clean data, joint training severely harms performance on these metrics. 
 Question: Which frozen acoustic model do they use?

A: a masking speech enhancement model BIBREF11, BIBREF12, BIBREF13
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Q: We published a survey on Reddit asking Danish speaking users to suggest offensive, sexist, and racist terms for a lexicon. Language and user behaviour varies between platforms, so the goal is to capture platform-specific terms. This gave 113 offensive and hateful terms which were used to find offensive comments. The remainder of comments in the corpus were shuffled and a subset of this corpus was then used to fill the remainder of the final dataset. The resulting dataset contains 3600 user-generated comments, 800 from Ekstra Bladet on Facebook, 1400 from r/DANMAG and 1400 from r/Denmark. 
 Question: How large was the dataset of Danish comments?

A: 3600 user-generated comments
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Q: For the evaluation of summaries we use the standard ROGUE metric. For comparison with previous AMR based summarization methods, we report the Recall, Precision and INLINEFORM0 scores for ROGUE-1. 
 Question: Which evaluation methods are used?

A:
Quantitative evaluation methods using ROUGE, Recall, Precision and F1.
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