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.

Input: Consider Input: The challenge is addressed as follows: given a natural language input sequence describing the scene, such as a piece of a story coming from a transcript, the goal is to infer which action is most likely to happen next.
Question: Do they literally just treat this as "predict the next spell that appears in the text"?

Output: Yes

Input: Consider Input: 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?

Output: 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

Input: Consider Input: The task of attribute embedding lays is embedding every value in attribute triples into a continuous vector space while preserving the semantic information. To capture both high-order structural information of KGs, we used an attention-based embedding propagation method. The final embedding of entities, relations and values are feed into two different deep neural network for two different tasks including link predication and entity classification.
Question: How does KANE capture both high-order structural and attribute information of KGs in an efficient, explicit and unified manner?
Output: To capture both high-order structural information of KGs, we used an attention-based embedding propagation method.