@inproceedings{tsygankova-etal-2019-bsnlp2019,
title = "{BSNLP}2019 Shared Task Submission: Multisource Neural {NER} Transfer",
author = "Tsygankova, Tatiana and
Mayhew, Stephen and
Roth, Dan",
editor = "Erjavec, Toma{\v{z}} and
Marci{\'n}czuk, Micha{\l} and
Nakov, Preslav and
Piskorski, Jakub and
Pivovarova, Lidia and
{\v{S}}najder, Jan and
Steinberger, Josef and
Yangarber, Roman",
booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-3710",
doi = "10.18653/v1/W19-3710",
pages = "75--82",
abstract = "This paper describes the Cognitive Computation (CogComp) Group{'}s submissions to the multilingual named entity recognition shared task at the Balto-Slavic Natural Language Processing (BSNLP) Workshop. The final model submitted is a multi-source neural NER system with multilingual BERT embeddings, trained on the concatenation of training data in various Slavic languages (as well as English). The performance of our system on the official testing data suggests that multi-source approaches consistently outperform single-source approaches for this task, even with the noise of mismatching tagsets.",
}
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%0 Conference Proceedings
%T BSNLP2019 Shared Task Submission: Multisource Neural NER Transfer
%A Tsygankova, Tatiana
%A Mayhew, Stephen
%A Roth, Dan
%Y Erjavec, Tomaž
%Y Marcińczuk, Michał
%Y Nakov, Preslav
%Y Piskorski, Jakub
%Y Pivovarova, Lidia
%Y Šnajder, Jan
%Y Steinberger, Josef
%Y Yangarber, Roman
%S Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F tsygankova-etal-2019-bsnlp2019
%X This paper describes the Cognitive Computation (CogComp) Group’s submissions to the multilingual named entity recognition shared task at the Balto-Slavic Natural Language Processing (BSNLP) Workshop. The final model submitted is a multi-source neural NER system with multilingual BERT embeddings, trained on the concatenation of training data in various Slavic languages (as well as English). The performance of our system on the official testing data suggests that multi-source approaches consistently outperform single-source approaches for this task, even with the noise of mismatching tagsets.
%R 10.18653/v1/W19-3710
%U https://aclanthology.org/W19-3710
%U https://doi.org/10.18653/v1/W19-3710
%P 75-82
Markdown (Informal)
[BSNLP2019 Shared Task Submission: Multisource Neural NER Transfer](https://aclanthology.org/W19-3710) (Tsygankova et al., BSNLP 2019)
ACL