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    <abstract>This paper describes Tübingen-Oslo team’s participation in the cross-lingual morphological analysis task in the VarDial 2019 evaluation campaign. We participated in the shared task with a standard neural network model. Our model achieved analysis F1-scores of 31.48 and 23.67 on test languages Karachay-Balkar (Turkic) and Sardinian (Romance) respectively. The scores are comparable to the scores obtained by the other participants in both language families, and the analysis score on the Romance data set was also the best result obtained in the shared task. Besides describing the system used in our shared task participation, we describe another, simpler, model based on linear classifiers, and present further analyses using both models. Our analyses, besides revealing some of the difficult cases, also confirm that the usefulness of a source language in this task is highly correlated with the similarity of source and target languages.</abstract>
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%0 Conference Proceedings
%T Neural and Linear Pipeline Approaches to Cross-lingual Morphological Analysis
%A Çöltekin, Çağrı
%A Barnes, Jeremy
%Y Zampieri, Marcos
%Y Nakov, Preslav
%Y Malmasi, Shervin
%Y Ljubešić, Nikola
%Y Tiedemann, Jörg
%Y Ali, Ahmed
%S Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects
%D 2019
%8 June
%I Association for Computational Linguistics
%C Ann Arbor, Michigan
%F coltekin-barnes-2019-neural
%X This paper describes Tübingen-Oslo team’s participation in the cross-lingual morphological analysis task in the VarDial 2019 evaluation campaign. We participated in the shared task with a standard neural network model. Our model achieved analysis F1-scores of 31.48 and 23.67 on test languages Karachay-Balkar (Turkic) and Sardinian (Romance) respectively. The scores are comparable to the scores obtained by the other participants in both language families, and the analysis score on the Romance data set was also the best result obtained in the shared task. Besides describing the system used in our shared task participation, we describe another, simpler, model based on linear classifiers, and present further analyses using both models. Our analyses, besides revealing some of the difficult cases, also confirm that the usefulness of a source language in this task is highly correlated with the similarity of source and target languages.
%R 10.18653/v1/W19-1416
%U https://aclanthology.org/W19-1416
%U https://doi.org/10.18653/v1/W19-1416
%P 153-164

Markdown (Informal)

[Neural and Linear Pipeline Approaches to Cross-lingual Morphological Analysis](https://aclanthology.org/W19-1416) (Çöltekin & Barnes, VarDial 2019)

• Neural and Linear Pipeline Approaches to Cross-lingual Morphological Analysis (Çöltekin & Barnes, VarDial 2019)

ACL

• Çağrı Çöltekin and Jeremy Barnes. 2019. Neural and Linear Pipeline Approaches to Cross-lingual Morphological Analysis. In Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects, pages 153–164, Ann Arbor, Michigan. Association for Computational Linguistics.