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	Rozumowanie oparte na zdrowym rozsądku w NLP	Odpowiadanie na pytania (QA)
Dziedzina	Eksploracja tekstu	Eksploracja tekstu
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	2019 (landmark benchmarks)	—
Twórca	Sap et al. (ATOMIC, 2019); Zellers et al. (HellaSwag, 2019)	—
Typ	NLP reasoning task	NLP text-comprehension task
Źródło pierwotne	Sap, M. et al. (2019). ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning. AAAI.	Rajpurkar, P. et al. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP.
Inne nazwy	commonsense NLP, if-then reasoning, Sağduyu Akıl Yürütme (Commonsense Reasoning)	QA, machine reading comprehension, Soru Cevaplama (Question Answering)
Pokrewne	6	4
Podsumowanie	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.	Question answering is a natural-language-processing task that automatically answers natural-language questions grounded in a given context passage, using either extractive or generative approaches. The task was crystallised by the SQuAD benchmark of Rajpurkar et al. (2016), and later models such as XLNet (Yang et al., 2019) pushed reading-comprehension accuracy higher.