@inproceedings{lange-etal-2020-choice,
title = "On the Choice of Auxiliary Languages for Improved Sequence Tagging",
author = {Lange, Lukas and
Adel, Heike and
Str{\"o}tgen, Jannik},
editor = "Gella, Spandana and
Welbl, Johannes and
Rei, Marek and
Petroni, Fabio and
Lewis, Patrick and
Strubell, Emma and
Seo, Minjoon and
Hajishirzi, Hannaneh",
booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.repl4nlp-1.13",
doi = "10.18653/v1/2020.repl4nlp-1.13",
pages = "95--102",
abstract = "Recent work showed that embeddings from related languages can improve the performance of sequence tagging, even for monolingual models. In this analysis paper, we investigate whether the best auxiliary language can be predicted based on language distances and show that the most related language is not always the best auxiliary language. Further, we show that attention-based meta-embeddings can effectively combine pre-trained embeddings from different languages for sequence tagging and set new state-of-the-art results for part-of-speech tagging in five languages.",
}
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%0 Conference Proceedings
%T On the Choice of Auxiliary Languages for Improved Sequence Tagging
%A Lange, Lukas
%A Adel, Heike
%A Strötgen, Jannik
%Y Gella, Spandana
%Y Welbl, Johannes
%Y Rei, Marek
%Y Petroni, Fabio
%Y Lewis, Patrick
%Y Strubell, Emma
%Y Seo, Minjoon
%Y Hajishirzi, Hannaneh
%S Proceedings of the 5th Workshop on Representation Learning for NLP
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F lange-etal-2020-choice
%X Recent work showed that embeddings from related languages can improve the performance of sequence tagging, even for monolingual models. In this analysis paper, we investigate whether the best auxiliary language can be predicted based on language distances and show that the most related language is not always the best auxiliary language. Further, we show that attention-based meta-embeddings can effectively combine pre-trained embeddings from different languages for sequence tagging and set new state-of-the-art results for part-of-speech tagging in five languages.
%R 10.18653/v1/2020.repl4nlp-1.13
%U https://aclanthology.org/2020.repl4nlp-1.13
%U https://doi.org/10.18653/v1/2020.repl4nlp-1.13
%P 95-102
Markdown (Informal)
[On the Choice of Auxiliary Languages for Improved Sequence Tagging](https://aclanthology.org/2020.repl4nlp-1.13) (Lange et al., RepL4NLP 2020)
ACL