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:
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?

Ex Output:
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


Ex Input:
EmotionLines BIBREF6 is a dialogue dataset composed of two subsets, Friends and EmotionPush, according to the source of the dialogues. 
 Question: what datasets were used?

Ex Output:
Friends EmotionPush


Ex Input:
Instead, our analysis reveals that this behavior is caused by subtle artifacts arising from the translation process itself. In particular, we show that translating different parts of each instance separately (e.g. the premise and the hypothesis in NLI) can alter superficial patterns in the data (e.g. the degree of lexical overlap between them), which severely affects the generalization ability of current models. Based on the gained insights, we improve the state-of-the-art in XNLI, and show that some previous findings need to be reconsidered in the light of this phenomenon. The benchmark consists of a competence test, which evaluates the ability to understand antonymy relation and perform numerical reasoning, a distraction test, which evaluates the robustness to shallow patterns like lexical overlap and the presence of negation words, and a noise test, which evaluates robustness to spelling errors. Just as with previous experiments, we report results for the best epoch checkpoint in each test set. 
 Question: What are examples of these artificats?

Ex Output:

the degree of lexical overlap between them presence of negation words