Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

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ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning (2021, methodology): “Relationships amongst different tasks Bingel & Søgaard (2017) conducted a study similar to ours in §2.1 but for more traditional NLP tasks like chunking, CCG tagging, POS tagging, etc.”
Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling (2018, background): “…results from the combination of translation and parsing, Subramanian et al. (2018) show the benefits of multitask learning in sentence-to-vector encoding, and Bingel & Søgaard (2017) and Changpinyo et al. (2018) offer studies of when multitask learning is helpful for lower-level NLP tasks.”
A Survey of Multilingual Neural Machine Translation (2019, background): “It has further been translated in more than 1,000 languages and used for multilingual studies [96, 111].”
Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data (2020, background): “…from one task to another, several works have proposed carefully choosing which tasks to train as an intermediate step in NLP before single task ﬁne-tuning (Bingel & Søgaard, 2017; Kerinec et al., 2018; Wang et al., 2019a; Standley et al., 2019; Pruksachatkun et al., 2020; Phang et al., 2018).”