TASK DEFINITION: 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.
PROBLEM: We also applied our model to MCTest dataset which requires machines to answer multiple-choice reading comprehension questions about fictional stories.  
 Question: Do they experiment with their proposed model on any other dataset other than MovieQA?

SOLUTION: Yes

PROBLEM: BERT-ADA BIBREF33 is a domain-adapted BERT-based model proposed for the APC task, which fine-tuned the BERT-BASE model on task-related corpus. This model obtained state-of-the-art accuracy on the Laptops dataset. We build a joint model for the multi-task of ATE and APC based on the BERT-BASE model. After optimizing the model parameters according to the empirical result, the joint model based on BERT-BASE achieved hopeful performance on all three datasets and even surpassed other proposed BERT based improved models on some datasets, such as BERT-PT, AEN-BERT, SDGCN-BERT, and so on. 
 Question: What was state of the art on SemEval-2014 task4 Restaurant and Laptop dataset?

SOLUTION: BERT-ADA BERT-PT, AEN-BERT, SDGCN-BERT

PROBLEM: In an offline step, we organize the content of each instance in a graph where each node represents a sentence from the passages and the question. Then, we add edges between nodes using the following topology:

we fully connect nodes that represent sentences from the same passage (dotted-black);

we fully connect nodes that represent the first sentence of each passage (dotted-red);

we add an edge between the question and every node for each passage (dotted-blue). 
 Question: How are some nodes initially connected based on text structure?

SOLUTION:
we fully connect nodes that represent sentences from the same passage we fully connect nodes that represent the first sentence of each passage we add an edge between the question and every node for each passage