Fynn Schröder


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Estimating the influence of auxiliary tasks for multi-task learning of sequence tagging tasks
Fynn Schröder | Chris Biemann
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Multi-task learning (MTL) and transfer learning (TL) are techniques to overcome the issue of data scarcity when training state-of-the-art neural networks. However, finding beneficial auxiliary datasets for MTL or TL is a time- and resource-consuming trial-and-error approach. We propose new methods to automatically assess the similarity of sequence tagging datasets to identify beneficial auxiliary data for MTL or TL setups. Our methods can compute the similarity between any two sequence tagging datasets, they do not need to be annotated with the same tagset or multiple labels in parallel. Additionally, our methods take tokens and their labels into account, which is more robust than only using either of them as an information source, as conducted in prior work. We empirically show that our similarity measures correlate with the change in test score of neural networks that use the auxiliary dataset for MTL to increase the main task performance. We provide an efficient, open-source implementation.


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Finding the way from ä to a: Sub-character morphological inflection for the SIGMORPHON 2018 shared task
Fynn Schröder | Marcel Kamlot | Gregor Billing | Arne Köhn
Proceedings of the CoNLL–SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection