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 Input:
he first dataset (WIKI) was the same set of 200 sentences from Wikipedia used in BIBREF7 .  The second dataset (SCI) was a set of 220 sentences from the scientific literature. We sourced the sentences from the OA-STM corpus. In total 2247 triples were extracted. In total, 11262 judgements were obtained after running the annotation process. 
 Question: What is the size of the released dataset?

Ex Output:
440 sentences, 2247 triples extracted from those sentences, and 11262 judgements on those triples.


Ex Input:
We train language models on two languages: One model is estimated on the German newscrawl distributed by WMT'18 comprising 260M sentences or 6B tokens. Another model is trained on the English newscrawl data comprising 193M sentences or 5B tokens. We learn a joint Byte-Pair-Encoding (BPE; Sennrich et al., 2016) vocabulary of 37K types on the German and English newscrawl and train the language models with this vocabulary. We consider two benchmarks: Most experiments are run on the WMT'18 English-German (en-de) news translation task and we validate our findings on the WMT'18 English-Turkish (en-tr) news task. For WMT'18 English-German, the training corpus consists of all available bitext excluding the ParaCrawl corpus and we remove sentences longer than 250 tokens as well as sentence-pairs with a source/target length ratio exceeding 1.5. This results in 5.18M sentence pairs. We tokenize all data with the Moses tokenizer BIBREF8 and apply the BPE vocabulary learned on the monolingual corpora. 
 Question: What dataset do they use?

Ex Output:
German newscrawl distributed by WMT'18  English newscrawl data WMT'18 English-German (en-de) news translation task  WMT'18 English-Turkish (en-tr) news task


Ex Input:
We recruited 100 crowdworkers on Amazon Mechanical Turk (AMT) and measured completion times and accuracies for typing randomly sampled sentences from the Yelp corpus.  
 Question: How many participants were trying this communication game?

Ex Output:

100