Few-shot learning methods have initially been introduced within the area of image classification (Vinyals et al., 2016;Ravi and Larochelle, 2017;Finn et al., 2017), but have recently also been applied to NLP tasks such as relation extraction (Han et al., 2018), text classification Rethmeier and Augenstein, 2020) and machine translation (Gu et al., 2018).
in NLP, these few-shot learning approaches include: (i) the transformation of the problem into a different task (e.g., relation extraction is transformed to question answering (Levy et al., 2017;Abdou et al., 2019)); or (ii) meta-learning (Andrychowicz et al., 2016;Finn et al., 2017).