Confronta i metodi

	Ragionamento di senso comune in PNL	Comprensione della lettura automatica (MRC)
Campo	Text mining	Text mining
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	2019 (landmark benchmarks)	2016
Ideatore	Sap et al. (ATOMIC, 2019); Zellers et al. (HellaSwag, 2019)	Rajpurkar, Zhang, Lopyrev & Liang (SQuAD)
Tipo	NLP reasoning task	NLP question-answering task
Fonte seminale	Sap, M. et al. (2019). ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning. AAAI.	Rajpurkar, P., Zhang, J., Lopyrev, K. & Liang, P. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP, 2383-2392.
Alias	commonsense NLP, if-then reasoning, Sağduyu Akıl Yürütme (Commonsense Reasoning)	MRC, question answering over passages, extractive question answering, Makine Okuma Anlama (MRC)
Correlati	6	3
Sintesi	Commonsense reasoning in NLP refers to the capacity of a language model or inference system to draw on implicit, world-knowledge facts that humans take for granted — facts not stated in the text — to answer questions, complete stories, or interpret dialogue. Landmark benchmarks formalising the task include ATOMIC (Sap et al., 2019), an if-then commonsense knowledge graph, and HellaSwag (Zellers et al., 2019), a sentence-completion challenge that exposed gaps in machine understanding of everyday events.	Machine reading comprehension (MRC), popularised by the SQuAD benchmark of Rajpurkar, Zhang, Lopyrev and Liang (2016), is a natural-language-processing task in which a model reads a given passage and answers multiple-choice or open-ended questions about it. It turns a passage plus a question into a machine-generated answer, supporting information retrieval, educational technology, and querying research databases.