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.

Example Input: In our experiments, we used WordNet 3.0 BIBREF9 as our external knowledge base INLINEFORM0 . For word embeddings, we experimented with two popular models: (1) GloVe embeddings trained by BIBREF10 on Wikipedia and Gigaword 5 (vocab: 400K, dim: 300), and (2) w2v-gn, Word2vec BIBREF5 trained on the Google News dataset (vocab: 3M, dim: 300). Our coverage enhancement starts by transforming the knowledge base INLINEFORM0 into a vector space representation that is comparable to that of the corpus-based space INLINEFORM1 . To this end, we use two techniques for learning low-dimensional feature spaces from knowledge graphs: DeepWalk and node2vec. DeepWalk uses a stream of short random walks in order to extract local information for a node from the graph. By treating these walks as short sentences and phrases in a special language, the approach learns latent representations for each node. Similarly, node2vec learns a mapping of nodes to continuous vectors that maximizes the likelihood of preserving network neighborhoods of nodes. Thanks to a flexible objective that is not tied to a particular sampling strategy, node2vec reports improvements over DeepWalk on multiple classification and link prediction datasets. For both these systems we used the default parameters and set the dimensionality of output representation to 100. Also, note than nodes in the semantic graph of WordNet represent synsets. Hence, a polysemous word would correspond to multiple nodes. In our experiments, we use the MaxSim assumption of BIBREF11 in order to map words to synsets. 
 Question: What other embedding models are tested?
Example Output: GloVe embeddings trained by BIBREF10 on Wikipedia and Gigaword 5 (vocab: 400K, dim: 300) w2v-gn, Word2vec BIBREF5 trained on the Google News dataset (vocab: 3M, dim: 300) DeepWalk  node2vec

Example Input: We chose SVMhmm BIBREF111 implementation of Structural Support Vector Machines for sequence labeling. SVM implementation of   
 Question: Which machine learning methods are used in experiments?
Example Output: Structural Support Vector Machine

Example Input: Further, we employ BLEU score to evaluate the accuracy of generations BIBREF21, and the number of distinct n-gram to evaluate the diversity of generations BIBREF6.  
 Question: How do they measure the diversity of inferences?
Example Output:
by number of distinct n-grams