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:
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 use Gaussian Processes as this probabilistic kernelised framework avoids the need for expensive cross-validation for hyperparameter selection 
 Question: Why is a Gaussian process an especially appropriate method for this classification problem?

Ex Output:
avoids the need for expensive cross-validation for hyperparameter selection


Ex Input:
Our model differs by learning the subword vectors and resulting representation jointly as weighted factorization of a word-context co-occurrence matrix is performed. 
 Question: Which matrix factorization methods do they use?

Ex Output:

weighted factorization of a word-context co-occurrence matrix