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.

[EX Q]: We have evaluated the output of Nefnir against a reference corpus of 21,093 tokens and their correct lemmas.

Samples for the reference corpus were extracted from two larger corpora, in order to obtain a diverse vocabulary:
Question: Which dataset do they use?
[EX A]: a reference corpus of 21,093 tokens and their correct lemmas

[EX Q]: We create a new manually annotated multimodal hate speech dataset formed by $150,000$ tweets, each one of them containing text and an image. We call the dataset MMHS150K, and made it available online . In this section, we explain the dataset creation steps.
Question: How many tweats does MMHS150k contains, 150000?
[EX A]: $150,000$ tweets

[EX Q]: Wav2vec BIBREF22 proposed a multi-layer convolutional neural network optimized via a noise contrastive binary classification and was applied to WSJ ASR tasks. Our experiments consisted of three different setups: 1) a fully-supervised system using all labeled data; 2) an SSL system using wav2vec features; 3) an SSL system using our proposed DeCoAR features. All models used were based on deep BLSTMs with the CTC loss criterion.
Question: What are baseline models on WSJ eval92 and LibriSpeech test-clean?
[EX A]:
Wav2vec BIBREF22 a fully-supervised system using all labeled data