You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Inspired by this new metric, in this work, we focus on building unsupervised zero-shot multiple-choice QA systems. That is, we target an arbitrary commonsense reasoning task where conventional approaches (that rely heavily on task-specific supervision) are not applicable to such zero-shot learning scenarios. To learn QA models without expensive annotation efforts, recent works (Ma et al., 2021; Banerjee and Baral, 2020; Malaviya et al., 2020) propose to generate a synthetic QA dataset using a commonsense KG such as ATOMIC (Sap et al., 2019a) and ConceptNet (Speer et al., 2017). Such an approach mostly focuses only on one specific type of reasoning relations (e.g., if-then relation, or declarative relation), neglecting the fact that real-world QA systems require simultaneously considering different types of reasoning abilities (e.g., declarative and social, or causal and physical reasoning; Ilievski et al., 2021; Chang et al., 2021).

Question: What is the main motivation of this work?
This work has been motivated from the fact that real-world QA systems require simultaneously considering different types of reasoning abilities. Therefore, this paper target an arbitrary commonsense reasoning task where conventional approaches are not applicable to such zero-shot learning scenarios.