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.

Automation of cQA forums can be divided into three tasks: question-comment relevance (Task A), question-question relevance (Task B), and question-external comment relevance (Task C). In our cQA tasks, the pair of objects are (question, question) or (question, comment), and the relationship is relevant/irrelevant. For task C, in addition to an original question (oriQ) and an external comment (relC), the question which relC commented on is also given (relQ). To incorporate this extra information, we consider a multitask learning framework which jointly learns to predict the relationships of the three pairs (oriQ/relQ, oriQ/relC, relQ/relC). 
 Question: What supplemental tasks are used for multitask learning?
Multitask learning is used for the task of predicting relevance of a comment on a different question to a given question, where the supplemental tasks are predicting relevance between the questions, and between the comment and the corresponding question

Suppose there are INLINEFORM0 mentions in documents on average, among these global models, NCEL not surprisingly has the lowest time complexity INLINEFORM1 since it only considers adjacent mentions, where INLINEFORM2 is the number of sub-GCN layers indicating the iterations until convergence. 
 Question: Do they only use adjacent entity mentions or use more than that in some cases (next to adjacent)?
NCEL considers only adjacent mentions.

We use two different unsupervised approaches for word sense disambiguation.  The second, called `dense model', represents synsets and contexts in a dense, low-dimensional space by averaging word embeddings. In the synset embeddings model approach, we follow SenseGram BIBREF14 and apply it to the synsets induced from a graph of synonyms.  We observe that the SenseGram-based approach for word sense disambiguation yields substantially better results in every case (Table TABREF25 ). The primary reason for that is the implicit handling of similar words due to the averaging of dense word vectors for semantically related words.  
 Question: Do the authors offer any hypothesis about why the dense mode outperformed the sparse one?

Yes