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]: Experimental Setup 
 Question: what english datasets were used?
[EX A]: Answer with content missing: (Data section) Penn Treebank (PTB)

[EX Q]: We sparsify this graph to the union of a minimum spanning tree and a k-Nearest Neighbors (MST-kNN) graph BIBREF14, a geometric construction that removes less important similarities but preserves global connectivity for the graph and, hence, for the dataset. The MST-kNN graph is then analysed with Markov Stability BIBREF15, BIBREF16, BIBREF17, BIBREF18, a multi-resolution graph partitioning method that identifies relevant subgraphs (i.e., clusters of documents) at different levels of granularity. 
 Question: What cluster identification method is used in this paper?
[EX A]: A combination of Minimum spanning trees, K-Nearest Neighbors and Markov Stability BIBREF15, BIBREF16, BIBREF17, BIBREF18

[EX Q]: We used the Levenshtein distance metric BIBREF8 implemented in Apache Lucene library BIBREF9 . Another simple approach is the aforementioned diacritical swapping, which is a term that we introduce here for referring to a solution inspired by the work of BIBREF4 . A promising method, adapted from work on correcting texts by English language learners BIBREF11 , expands on the concept of selecting a correction nearest to the spelling error according to some notion of distance. Here, the Levenshtein distance is used in a weighted sum to cosine distance between word vectors. Our ELMo-augmented LSTM is bidirectional. 
 Question: What methods are tested in PIEWi?
[EX A]:
Levenshtein distance metric BIBREF8 diacritical swapping Levenshtein distance is used in a weighted sum to cosine distance between word vectors ELMo-augmented LSTM