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.
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Question: However, as shown in Figure 1 , to assess the similarity between two research papers, a more effective strategy would compare and align (via local-weighting) individual important words (keywords) within a pair of abstracts, while information from other words (e.g., stop words) that tend to be less relevant can be effectively ignored (down-weighted).  We propose to learn a semantic-aware Network Embedding (NE) that incorporates word-level alignment features abstracted from text sequences associated with vertex pairs. Given a pair of sentences, our model first aligns each word within one sentence with keywords from the other sentence (adaptively up-weighted via an attention mechanism), producing a set of fine-grained matching vectors 
 Question: What text sequences are associated with each vertex?

Answer: abstracts sentences


Question: jiant System Overview ::: jiant Components
Tasks: Tasks have references to task data, methods for processing data, references to classifier heads, and methods for calculating performance metrics, and making predictions. 
 Question: Does jiant involve datasets for the 50 NLU tasks?

Answer: Yes


Question: In this study we use transcripts and results of Oxford-style debates from the public debate series “Intelligence Squared Debates” (IQ2 for short). 
 Question: what debates dataset was used?

Answer:
Intelligence Squared Debates