In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Input: Consider Input: For the image-aided model (W+C+V; upper row in Figure FIGREF19 ), we confirm that the modality attention successfully attenuates irrelevant signals (selfies, etc.) and amplifies relevant modality-based contexts in prediction of a given token.

Output: Do they inspect their model to see if their model learned to associate image parts with words related to entities?


Input: Consider Input: When evaluating classifiers it is common to use accuracy, precision and recall as well as Hamming loss.  It has been shown that when calculating precision and recall on multi-label classifiers, it can be advantageous to use micro averaged precision and recall BIBREF6 . The formulas for micro averaged precision are expressed as DISPLAYFORM0 DISPLAYFORM1

Output: what evaluation metrics are discussed?


Input: Consider Input: Task processing: converting data exploration tasks to algebraic operations on the embedding space by following task-specific conversion templates. Some important tasks and their conversion templates are discussed in Section SECREF5.

Query processing: executing semantic query on the embedding space and return results. Note that the algebraic operations on embedding vectors are linear and can be performed in parallel. Therefore, the semantic query is efficient.


Output: What data exploration is supported by the analysis of these semantic structures?