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 compare our model with the following state-of-the-art joint entity and relation extraction models:

(1) SPTree BIBREF4: This is an end-to-end neural entity and relation extraction model using sequence LSTM and Tree LSTM. (2) Tagging BIBREF5: This is a neural sequence tagging model which jointly extracts the entities and relations using an LSTM encoder and an LSTM decoder. (3) CopyR BIBREF6: This model uses an encoder-decoder approach for joint extraction of entities and relations. (4) HRL BIBREF11: This model uses a reinforcement learning (RL) algorithm with two levels of hierarchy for tuple extraction. (5) GraphR BIBREF14: This model considers each token in a sentence as a node in a graph, and edges connecting the nodes as relations between them. (6) N-gram Attention BIBREF9: This model uses an encoder-decoder approach with N-gram attention mechanism for knowledge-base completion using distantly supervised data. 
 Question: What is previous work authors reffer to?

Ex Output:
SPTree Tagging CopyR HRL GraphR N-gram Attention


Ex Input:
For the Russian language, with its rich morphology, lemmatizing the training and testing data for ELMo representations yields small but consistent improvements in the WSD task.  
 Question: What other examples of morphologically-rich languages do the authors give?

Ex Output:
Russian


Ex Input:
We carried out a reliability study for the proposed scheme using two pairs of expert annotators, P1 and P2.  Inter-rater reliability was assessed using Cohen's kappa: unweighted for argumentation and knowledge domain, but quadratic-weighted for specificity given its ordered labels. 
 Question: what experiments are conducted?

Ex Output:

a reliability study for the proposed scheme 


