Sammenlign metoder
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| Maskinlæsningsforståelse (MRC)× | Domænetilpasning× | |
|---|---|---|
| Fagområde | Tekstmining | Tekstmining |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 2016 | — |
| Ophavsperson≠ | Rajpurkar, Zhang, Lopyrev & Liang (SQuAD) | — |
| Type≠ | NLP question-answering task | NLP transfer-learning / fine-tuning pipeline |
| Oprindelig kilde≠ | Rajpurkar, P., Zhang, J., Lopyrev, K. & Liang, P. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP, 2383-2392. DOI ↗ | Lee, J. et al. (2020). BioBERT: A Pre-trained Biomedical Language Representation Model. Bioinformatics. DOI ↗ |
| Aliasser≠ | MRC, question answering over passages, extractive question answering, Makine Okuma Anlama (MRC) | Alan Uyarlaması (Domain Adaptation) — NLP, domain adaptation NLP, domain fine-tuning |
| Relaterede≠ | 3 | 4 |
| Resumé≠ | 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. | Domain adaptation is a natural-language-processing technique that takes a general pretrained language model and fine-tunes it on target-domain data so that it performs better in specialised fields such as medicine, law, and finance. It builds on the transfer-learning ideas behind work like Blitzer et al. (2007) on cross-domain sentiment classification and Lee et al. (2020) on the biomedical BioBERT model. |
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