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.

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?

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
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Q: Encoders with induced latent structures have been shown to benefit several tasks including document classification, natural language inference BIBREF12, BIBREF13, and machine translation BIBREF11.  
 Question: Is there any evidence that encoders with latent structures work well on other tasks?

A: Yes
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Q: Prior to the SAOKE data set, an annotated data set for OIE tasks with 3,200 sentences in 2 domains was released in BIBREF20 to evaluate OIE algorithms, in which the data set was said BIBREF20 “13 times larger than the previous largest annotated Open IE corpus”. 
 Question: What's the size of the previous largest OpenIE dataset?

A:
3,200 sentences
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