Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Razonamiento de sentido común en PLN× | Comprensión Lectora Automática (MRC)× | |
|---|---|---|
| Campo | Minería de texto | Minería de texto |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 2019 (landmark benchmarks) | 2016 |
| Autor original≠ | Sap et al. (ATOMIC, 2019); Zellers et al. (HellaSwag, 2019) | Rajpurkar, Zhang, Lopyrev & Liang (SQuAD) |
| Tipo≠ | NLP reasoning task | NLP question-answering task |
| Fuente seminal≠ | 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 ↗ |
| Alias≠ | commonsense NLP, if-then reasoning, Sağduyu Akıl Yürütme (Commonsense Reasoning) | MRC, question answering over passages, extractive question answering, Makine Okuma Anlama (MRC) |
| Relacionados≠ | 6 | 3 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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