{"type":"ScholarlyArticle","title":"Transferability and Stability of Learning with Limited Labelled Data in Multilingual Text Document Classification","authors":[{"name":"Branislav Pecher","slug":"branislav-pecher","given":"Branislav","family":"Pecher","affiliation":"Faculty of Information Technology , Brno University of Technology , Brno , Czech Republic; Kempelen Institute of Intelligent Technologies , Mlynske nivy 5 , Bratislava , Slovakia"}],"abstract":"We focus on learning with limited labelled data (especially meta-learning) in conjunction with so-far under-researched multilingual textual document classification. The core principle in such learning is to achieve transferability of learned knowledge to new datasets and tasks. Currently, factors influencing the success of transfer remain mostly unclear. Their identification from experiments is challenging due to small amounts of labels making results considerably unstable. When instability of the investigated models is not explicitly taken into consideration (as is common in existing benchmarking studies), it may result in randomness possibly even invalidating the findings. We want to remedy this by in-depth exploration of factors that influence the stability and the transferability of learning with limited labelled data in multilingual textual documents classification, such as misinformation detection.","doi":"10.24963/ijcai.2022/837","url":"https://ijcai.org/proceedings/2022/837","pdf":"https://ijcai.org/proceedings/2022/0837.pdf","markdown":"https://ijcai.org/proceedings/2022/837.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/837","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":837,"pages":{"first":5869,"last":5870},"keywords":["Machine Learning (ML): General"],"videos":[],"references":[{"position":1,"raw":"[Boquet et al., 2019] Thomas Boquet, Laure Delisle, De- nis Kochetkov, Nathan Schucher, Boris N Oreshkin, and Julien Cornebise. Reproducibility and stability analysis in metric-based few-shot learning. RML@ ICLR, 2019.","title":"Reproducibility and stability analysis in metric-based few-shot learning","authors":"Thomas Boquet, Laure Delisle, Denis Kochetkov, Nathan Schucher, Boris N Oreshkin, Julien Cornebise","year":"2019","venue":"RML@ ICLR","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Hospedales et al., 2022] Timothy Hospedales, Antreas An- toniou, Paul Micaelli, and Amos Storkey. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2022.","title":"Timothy Hospedales, Antreas Antoniou","authors":"Hospedales","year":"2022","venue":"IEEE Transactions on Pattern Analysis & Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Lu et al., 2021] Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. Fantastically ordered prompts and where to find them: Overcom- ing few-shot prompt order sensitivity. arXiv preprint arXiv:2104.08786, 2021.","title":"Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity","authors":"Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, Pontus Stenetorp","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Ye et al., 2021] Qinyuan Ye, Bill Yuchen Lin, and Xiang Ren. CrossFit: A few-shot learning challenge for cross- task generalization in NLP. In Proc. of the 2021 Confer- ence on Empirical Methods in Natural Language Process- ing. Association for Computational Linguistics, 2021.","title":"CrossFit: A few-shot learning challenge for crosstask generalization in NLP","authors":"Ye","year":"2021","venue":"Proc. of the 2021 Conference on Empirical Methods in Natural Language Processing","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}