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: 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).

SOLUTION: How are some nodes initially connected based on text structure?

PROBLEM: In the future, we aim to use the corpus for annotation projects such as parts-of-speech tagging, named entity recognition. Furthermore, the generated word embeddings will be utilized for the automatic construction of Sindhi WordNet.

SOLUTION: Are trained word embeddings used for any other NLP task?

PROBLEM: It should also be noted that signals can be recognized even when the model misclassifies relations, since ${\Delta }_s$ does not rely on correct classification: it merely quantifies the contribution of a word in context toward the correct label's score. If we examine the influence of each word on the score of the correct relation, that impact should and does still correlate with human judgments based on what the system may tag as the second or third best class to choose.

SOLUTION:
Where does proposed metric overlap with juman judgement?