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.

Our pool of annotators is selected after several short training rounds, with up to 15 predicates per round, in which they received extensive personal feedback.
Question: How are workers trained?
extensive personal feedback

We evaluated the proposed model using the publicly available LibriSpeech ASR corpus BIBREF23. The LibriSpeech dataset consists of 970 hours of audio data with corresponding text transcripts (around 10M word tokens) and an additional 800M word token text only dataset.
Question: How big is LibriSpeech dataset?
970 hours of audio data with corresponding text transcripts (around 10M word tokens) and an additional 800M word token text only dataset

To evaluate the responses generated by all compared methods, we compute the following automatic metrics on our test set:

BLEU: BLEU-n measures the average n-gram precision on a set of reference responses. We report BLEU-n with n=1,2,3,4.

Distinct-1 & distinct-2 BIBREF5: We count the numbers of distinct uni-grams and bi-grams in the generated responses and divide the numbers by the total number of generated uni-grams and bi-grams in the test set. These metrics can be regarded as an automatic metric to evaluate the diversity of the responses.
Question: What automatic metrics are used?
BLEU Distinct-1 & distinct-2