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: A recently published dataset called SCAN BIBREF2 (Simplified version of the CommAI Navigation tasks), tests compositional generalization in a sequence-to-sequence (seq2seq) setting by systematically holding out of the training set all inputs containing a basic primitive verb ("jump"), and testing on sequences containing that verb.
Question: How does the SCAN dataset evaluate compositional generalization?

A: it systematically holds out inputs in the training set containing basic primitive verb, "jump", and tests on sequences containing that verb.
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Q: We use the Bayesian model of garg2012unsupervised as our base monolingual model. The semantic roles are predicate-specific.
Question: What does an individual model consist of?

A: Bayesian model of garg2012unsupervised as our base monolingual model
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Q: Our approach consists of structuring input to the transformer network to use and guide the self-attention of the transformers, conditioning it on the entity. Our main mode of encoding the input, the entity-first method, is shown in Figure FIGREF4. We also have an entity-last variant where the entity is primarily observed just before the classification token to condition the [CLS] token's self-attention accordingly. As an additional variation, we can either run the transformer once per document with multiple [CLS] tokens (a document-level model as shown in Figure FIGREF4) or specialize the prediction to a single timestep (a sentence-level model).
Question: In what way is the input restructured?

A:
In four entity-centric ways - entity-first, entity-last, document-level and sentence-level
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