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: 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?

SOLUTION: abstracts sentences

PROBLEM: The main set of experiments uses a Linear Support Vector Machine (Joachims, 1998) to classify dialects using CxG features. This paper has used data-driven language mapping to select national dialects of English to be included in a global dialect identification model.
Question: What do the models that they compare predict?

SOLUTION: national dialects of English

PROBLEM: To address our next question, we assessed our pipeline when learning relations independently (i.e., individually) versus learning relations jointly within the RDN, displayed in Table TABREF22 .
Question: What do they learn jointly?

SOLUTION:
relations