TASK DEFINITION: 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.
PROBLEM: Note that GANE-AP delivers better results compared with GANE-OT, suggesting the attention parsing mechanism can further improve the low-level mutual attention matrix.

SOLUTION: Which of their proposed attention methods works better overall?

PROBLEM: To study how the multimodal context can boost the performance compared to an unimodal context we evaluate different models: a Feature Concatenation Model (FCM), a Spatial Concatenation Model (SCM) and a Textual Kernels Model (TKM)

SOLUTION: What models do they propose?

PROBLEM: In this section, we evaluate our method and compare its performance against the competitive approaches. We use INLINEFORM0 -fold evaluation protocol with INLINEFORM1 with random dataset split. We measure the performance using standard accuracy metric which we define as a ratio between correctly classified data samples from test dataset and all test samples.

SOLUTION:
What evaluation metrics are used?