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 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?

Answer: SPTree Tagging CopyR HRL GraphR N-gram Attention


Question: We took inspiration from these works to design our experiments to solve the CSKS task. 
 Question: What problem do they apply transfer learning to?

Answer: CSKS task


Question: to answer how offensive they thought the tweet was on a 6-point Likert scale from 1 (Not offensive at all) to 6 (Very offensive). If they answered 4 or higher, the participants had the option to state which particular words they found offensive. 
 Question: Was the degree of offensiveness taken as how generally offensive the text was, or how personally offensive it was to the annotator?

Answer:
Personal thought of the annotator.