@inproceedings{winata-etal-2019-learning,
title = "Learning Multilingual Meta-Embeddings for Code-Switching Named Entity Recognition",
author = "Winata, Genta Indra and
Lin, Zhaojiang and
Fung, Pascale",
editor = "Augenstein, Isabelle and
Gella, Spandana and
Ruder, Sebastian and
Kann, Katharina and
Can, Burcu and
Welbl, Johannes and
Conneau, Alexis and
Ren, Xiang and
Rei, Marek",
booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4320",
doi = "10.18653/v1/W19-4320",
pages = "181--186",
abstract = "In this paper, we propose Multilingual Meta-Embeddings (MME), an effective method to learn multilingual representations by leveraging monolingual pre-trained embeddings. MME learns to utilize information from these embeddings via a self-attention mechanism without explicit language identification. We evaluate the proposed embedding method on the code-switching English-Spanish Named Entity Recognition dataset in a multilingual and cross-lingual setting. The experimental results show that our proposed method achieves state-of-the-art performance on the multilingual setting, and it has the ability to generalize to an unseen language task.",
}
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%0 Conference Proceedings
%T Learning Multilingual Meta-Embeddings for Code-Switching Named Entity Recognition
%A Winata, Genta Indra
%A Lin, Zhaojiang
%A Fung, Pascale
%Y Augenstein, Isabelle
%Y Gella, Spandana
%Y Ruder, Sebastian
%Y Kann, Katharina
%Y Can, Burcu
%Y Welbl, Johannes
%Y Conneau, Alexis
%Y Ren, Xiang
%Y Rei, Marek
%S Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F winata-etal-2019-learning
%X In this paper, we propose Multilingual Meta-Embeddings (MME), an effective method to learn multilingual representations by leveraging monolingual pre-trained embeddings. MME learns to utilize information from these embeddings via a self-attention mechanism without explicit language identification. We evaluate the proposed embedding method on the code-switching English-Spanish Named Entity Recognition dataset in a multilingual and cross-lingual setting. The experimental results show that our proposed method achieves state-of-the-art performance on the multilingual setting, and it has the ability to generalize to an unseen language task.
%R 10.18653/v1/W19-4320
%U https://aclanthology.org/W19-4320
%U https://doi.org/10.18653/v1/W19-4320
%P 181-186
Markdown (Informal)
[Learning Multilingual Meta-Embeddings for Code-Switching Named Entity Recognition](https://aclanthology.org/W19-4320) (Winata et al., RepL4NLP 2019)
ACL