TASK DEFINITION: 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.
PROBLEM: 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?

SOLUTION: 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

PROBLEM: Models include both neural nets (e.g. RNNs, CNNs) and standard machine learning tools (e.g. Naive Bayes with Laplace Smoothing, k-clustering, SVM with linear kernel).
Question: Which machine learning models are used?

SOLUTION: RNNs CNNs Naive Bayes with Laplace Smoothing k-clustering SVM with linear kernel

PROBLEM: When evaluating classifiers it is common to use accuracy, precision and recall as well as Hamming loss. It has been shown that when calculating precision and recall on multi-label classifiers, it can be advantageous to use micro averaged precision and recall BIBREF6 . The formulas for micro averaged precision are expressed as DISPLAYFORM0 DISPLAYFORM1
Question: what evaluation metrics are discussed?

SOLUTION:
precision recall Hamming loss micro averaged precision and recall