@inproceedings{ozates-cetinoglu-2021-language,
title = "A Language-aware Approach to Code-switched Morphological Tagging",
author = {{\"O}zate{\c{s}}, {\c{S}}aziye Bet{\"u}l and
{\c{C}}etino{\u{g}}lu, {\"O}zlem},
editor = "Solorio, Thamar and
Chen, Shuguang and
Black, Alan W. and
Diab, Mona and
Sitaram, Sunayana and
Soto, Victor and
Yilmaz, Emre and
Srinivasan, Anirudh",
booktitle = "Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.calcs-1.10",
doi = "10.18653/v1/2021.calcs-1.10",
pages = "72--83",
abstract = "Morphological tagging of code-switching (CS) data becomes more challenging especially when language pairs composing the CS data have different morphological representations. In this paper, we explore a number of ways of implementing a language-aware morphological tagging method and present our approach for integrating language IDs into a transformer-based framework for CS morphological tagging. We perform our set of experiments on the Turkish-German SAGT Treebank. Experimental results show that including language IDs to the learning model significantly improves accuracy over other approaches.",
}
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<abstract>Morphological tagging of code-switching (CS) data becomes more challenging especially when language pairs composing the CS data have different morphological representations. In this paper, we explore a number of ways of implementing a language-aware morphological tagging method and present our approach for integrating language IDs into a transformer-based framework for CS morphological tagging. We perform our set of experiments on the Turkish-German SAGT Treebank. Experimental results show that including language IDs to the learning model significantly improves accuracy over other approaches.</abstract>
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%0 Conference Proceedings
%T A Language-aware Approach to Code-switched Morphological Tagging
%A Özateş, Şaziye Betül
%A Çetinoğlu, Özlem
%Y Solorio, Thamar
%Y Chen, Shuguang
%Y Black, Alan W.
%Y Diab, Mona
%Y Sitaram, Sunayana
%Y Soto, Victor
%Y Yilmaz, Emre
%Y Srinivasan, Anirudh
%S Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F ozates-cetinoglu-2021-language
%X Morphological tagging of code-switching (CS) data becomes more challenging especially when language pairs composing the CS data have different morphological representations. In this paper, we explore a number of ways of implementing a language-aware morphological tagging method and present our approach for integrating language IDs into a transformer-based framework for CS morphological tagging. We perform our set of experiments on the Turkish-German SAGT Treebank. Experimental results show that including language IDs to the learning model significantly improves accuracy over other approaches.
%R 10.18653/v1/2021.calcs-1.10
%U https://aclanthology.org/2021.calcs-1.10
%U https://doi.org/10.18653/v1/2021.calcs-1.10
%P 72-83
Markdown (Informal)
[A Language-aware Approach to Code-switched Morphological Tagging](https://aclanthology.org/2021.calcs-1.10) (Özateş & Çetinoğlu, CALCS 2021)
ACL