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 Q]: One way to analyze the model is to compute model gradients with respect to input features BIBREF26, BIBREF25. Figure FIGREF37 shows that in this particular example, the most important model inputs are verbs possibly associated with the entity butter, in addition to the entity's mentions themselves. It further shows that the model learns to extract shallow clues of identifying actions exerted upon only the entity being tracked, regardless of other entities, by leveraging verb semantics. 
 Question: What evidence do they present that the model attends to shallow context clues?
[EX A]: Using model gradients with respect to input features they presented that the most important model inputs are verbs associated with entities which shows that the model attends to shallow context clues

[EX Q]: Cloze Track User Query Track 
 Question: What two types the Chinese reading comprehension dataset consists of?
[EX A]: cloze-style reading comprehension and user query reading comprehension questions

[EX Q]: For the future work, we plan to solve the triples with multiple entities as the second entity, which is excluded from problem scope in this paper. The input of QA4IE is a document $D$ with an existing knowledge base $K$ and the output is a set of relation triples $R = \lbrace e_i, r_{ij}, e_j\rbrace $ in $D$ where $e_i$ and $e_j$ are two individual entities and $r_{ij}$ is their relation. 
 Question: Can this approach model n-ary relations?
[EX A]:
No