Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Commonsense Reasoning× | Машинно разбиране на текст (MRC)× | |
|---|---|---|
| Област | Извличане на текст | Извличане на текст |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 2019 (landmark benchmarks) | 2016 |
| Създател≠ | Sap et al. (ATOMIC, 2019); Zellers et al. (HellaSwag, 2019) | Rajpurkar, Zhang, Lopyrev & Liang (SQuAD) |
| Тип≠ | NLP reasoning task | NLP question-answering task |
| Основополагащ източник≠ | Sap, M. et al. (2019). ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning. AAAI. link ↗ | Rajpurkar, P., Zhang, J., Lopyrev, K. & Liang, P. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP, 2383-2392. DOI ↗ |
| Други названия≠ | commonsense NLP, if-then reasoning, Sağduyu Akıl Yürütme (Commonsense Reasoning) | MRC, question answering over passages, extractive question answering, Makine Okuma Anlama (MRC) |
| Свързани≠ | 6 | 3 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
|
|