Table 2: An organization of works on meta-learning in NLP. The Application column lists the applications that are performed in corresponding papers. We use the following abbreviations. QA: Question Answering. MT: Machine Translation. TC: Text Classification (including Natural Language Inference). IE: Information Extraction (including Relation Classificaiton and Knowledge Graph Completion). WE: Word Ebedding TAG: Sequence Tagging. PAR: Parsing. DST: Dialogoue State Tracking. DG: Dialogoue Generation (including Natural Language Generation). MG: Multimodal Grounding. ASR: Automatic Speech Recognition. SS: Source Separation. KS: Keyword Spotting. VC: Voice Cloning. SED: Sound Event Detection. The Method column lists the involving meta-learning methods. INIT is learning to initialize; COM is learning to compare; NAS is network architecture search; OPT is learning to optimize; ALG is learning the learning algorithm; SEL is learning to select data. Task construction column lists the way each work is built for training meta-parameters. Please refer to Section 3 for the description about task construction.
Work	Method	Application	Task construction
(Dou et al., 2019)	INIT	TC	Cross-problem
(Bansal et al., 2020a)	INIT	TC	Cross-problem
(Holla et al., 2020)	INIT	TC	A task includes sentences containing the same word with different senses.
(Zhou et al., 2022)	INIT	TC	Knowledge Distallation
(Pan et al., 2020)	COM	TC	Knowledge Distallation
(van der Heijden et al., 2021)	INIT	TC	Cross-lingual
(Bansal et al., 2020b)	INIT	TC	Cross-problem (some tasks are generated in an self-supervised way)
(Murty et al., 2021)	INIT	TC	Cross-problem
(Wang et al., 2021b)	INIT	TC, DST	Cross-domain
(Yu et al., 2018)	COM	TC	Cross-domain
(Tan et al., 2019)	COM	TC	Cross-domain
(Geng et al., 2019)	COM	TC	Cross-domain
(Sun et al., 2019b)	COM	TC	The tasks are seperated by class labels.
(Geng et al., 2020)	COM	TC	The tasks are seperated by class labels.
(Li et al., 2020c)	COM	TC	Domain Generalization
(Wu et al., 2019)	OPT	TC	Monolithic
(Pasunuru and Bansal, 2020)	NAS	TC	Monolithic
(Pasunuru and Bansal, 2019)	NAS	TC	Monolithic
(Xu et al., 2021c)	OPT	TC	Domain Generalization
(Zheng et al., 2021)	SEL	TC	Monolithic
(Wu et al., 2020)	INIT	TAG	Cross-lingual
(Xia et al., 2021)	INIT	TC, TAG	Cross-lingual
(Hou et al., 2020)	COM	TAG	Cross-domain
(Oguz and Vu, 2021)	COM	TAG	The tasks are seperated by class labels.
(Li et al., 2020b)	NAS	DG	Monolithic
(Jiang et al., 2019)	NAS	TAG	Monolithic
(Obamuyide and Vlachos, 2019b)	INIT	IE	Each task includes the examples for a relation.
(Bose et al., 2020)	INIT	IE	Each task is a graph.
(Lv et al., 2019)	INIT	IE	Each task includes the examples for a relation.
(Chen et al., 2019a)	COM	IE	Each task includes the examples for a relation.
(Gao et al., 2019a)	COM	IE	Each task includes the examples for a relation.
(Ren et al., 2020)	COM	IE	Each task includes the examples for a relation.
(Xiong et al., 2018)	COM	IE	Each task includes the examples for a relation.
(Wang et al., 2019b)	INIT	IE	Each task includes the examples for a relation.
(Zhang et al., 2020)	COM	IE	Each task includes the examples for a relation.
(Sheng et al., 2020)	COM	IE	Each task includes the examples for a relation.
(Hu et al., 2019)	INIT	WE	Each task includes the context of a word.
(Sun et al., 2018)	COM	WE	Each task includes the context of a word.
(M'hamdi et al., 2021)	INIT	QA, TAG	Cross-lingual, Domain Generalization
(Nooralahzadeh et al., 2020)	INIT	QA, TC	Cross-lingual
(Yan et al., 2020)	INIT	QA	Cross-domain
(Gu et al., 2018)	INIT	MT	Cross-lingual
(Indurthi et al., 2020)	INIT	MT	Cross-problem
(Li et al., 2020a)	INIT	MT	Cross-domain
(Park et al., 2021)	INIT	MT	Cross-domain
(Wang et al., 2020b)	SEL	MT	Monolithic