instruction:
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.
question:
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.
answer:
Do they inspect their model to see if their model learned to associate image parts with words related to entities?

question:
Where possible, we tried to restrict the content of each text to the abstract and conclusion of the original work. However, since in many cases these sections were too short, we also considered other parts of the original works such as introduction or discussion sections. To ensure that the extracted text portions are appropriate for the AV task, each original work was preprocessed manually. More precisely, we removed tables, formulas, citations, quotes and sentences that include non-language content such as mathematical constructs or specific names of researchers, systems or algorithms.
answer:
What is a self-compiled corpus?

question:
$Coherence@k$ has been shown to have high correlation with human interpretability of topics learned via various topic modeling methods BIBREF7 . Hence, we can expect interpretable embeddings by maximizing it. For evaluating the interpretability, we use $Coherence@k$ (Equation 6 ) , automated and manual word intrusion tests.
answer:
How do they evaluate interpretability?