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      Open Problems in Computational Historical Linguistics

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          Abstract

          Problems constitute the starting point of all scientific research. The essay reflects on the different kinds of problems that scientists address in their research and discusses a list of 10 problems for the field of computational historical linguistics, that was proposed throughout 2019 in a series of blog posts. In contrast to problems identified in different contexts, these problems were considered to be solvable, but no solution could be proposed back then. By discussing the problems in the light of developments that have been made in the field during the past five years, a modified list is proposed that takes new insights into account but also finds that the majority of the problems has not yet been solved.

          Plain language summary

          This essay elaborates on ten problems for the field of historical linguistics, dealing with the evolution of languages, which have so far not been solved. It is argued that the solution of these problems is possible, but that additional work is needed before breakthroughs can be made.

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          Attention Is All You Need

          The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. 15 pages, 5 figures
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            On aims and methods of Ethology

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              Binary codes capable of correcting deletions, insertions, and reversals

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal AnalysisRole: Funding AcquisitionRole: InvestigationRole: Project AdministrationRole: ResourcesRole: VisualizationRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Journal
                Open Res Eur
                Open Res Eur
                Open Research Europe
                F1000 Research Limited (London, UK )
                2732-5121
                20 November 2023
                2023
                : 3
                : 201
                Affiliations
                [1 ]Chair of Multilingual Computational Linguistics, University of Passau, Passau, Bavaria, 94032, Germany
                [2 ]Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, 04103, Germany
                [1 ]Germantistische Linguistik, Albert-Ludwigs Universität, Freiburg, Germany
                [2 ]Computational Linguistics and chair of Humanities Computing, University of Groningen, Groningen, The Netherlands
                [1 ]Australian National University, Canberra, Australian Capital Territory, Australia
                [2 ]The University of Queensland, Saint Lucia, Queensland, Australia
                [1 ]Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
                [1 ]University of Buffalo, Buffalo, USA
                Author notes

                No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Author information
                https://orcid.org/0000-0003-2133-8919
                Article
                10.12688/openreseurope.16804.1
                10864822
                38357681
                ac4a4532-c8e8-4885-bd05-b28f24fabeff
                Copyright: © 2023 List JM

                This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 10 November 2023
                Funding
                Funded by: Horizon Europe Framework Programme
                Award ID: 101044282
                Funded by: Max-Planck-Gesellschaft
                Award ID: ResearchGrantCALC³
                This project has received funding from the European Research Council (ERC) under the [European Unions Horizon Europe research and innovation programme] (Grant agreement No. 101044282).
                The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Essay
                Articles

                historical linguistics,computational linguistics,open problems,scientific problem solving

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