instruction:
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.
question:
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?
answer:
A combination of Minimum spanning trees, K-Nearest Neighbors and Markov Stability BIBREF15, BIBREF16, BIBREF17, BIBREF18


question:
Pre-trained English NER model We construct the English NER system following BIBREF7 . This system uses a bidirectional LSTM as a character-level language model to take context information for word embedding generation.  For pre-trained English NER system, we use the default NER model of Flair. 
 Question: Which pre-trained English NER model do they use?
answer:
Bidirectional LSTM based NER model of Flair


question:
We began with evaluating standard MT paradigms, i.e., PBSMT BIBREF3 and NMT BIBREF1 . As for PBSMT, we also examined two advanced methods: pivot-based translation relying on a helping language BIBREF10 and induction of phrase tables from monolingual data BIBREF14 .

As for NMT, we compared two types of encoder-decoder architectures: attentional RNN-based model (RNMT) BIBREF2 and the Transformer model BIBREF18 . In addition to standard uni-directional modeling, to cope with the low-resource problem, we examined two multi-directional models: bi-directional model BIBREF11 and multi-to-multi (M2M) model BIBREF8 .

After identifying the best model, we also examined the usefulness of a data augmentation method based on back-translation BIBREF17 . 
 Question: what was the baseline?
answer:

pivot-based translation relying on a helping language BIBREF10 nduction of phrase tables from monolingual data BIBREF14  attentional RNN-based model (RNMT) BIBREF2 Transformer model BIBREF18 bi-directional model BIBREF11 multi-to-multi (M2M) model BIBREF8 back-translation BIBREF17