@inproceedings{moran-lignos-2020-effective,
title = "Effective Architectures for Low Resource Multilingual Named Entity Transliteration",
author = "Moran, Molly and
Lignos, Constantine",
booktitle = "Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.loresmt-1.11",
pages = "79--86",
abstract = "In this paper, we evaluate LSTM, biLSTM, GRU, and Transformer architectures for the task of name transliteration in a many-to-one multilingual paradigm, transliterating from 590 languages to English. We experiment with different encoder-decoder combinations and evaluate them using accuracy, character error rate, and an F-measure based on longest continuous subsequences. We find that using a Transformer for the encoder and decoder performs best, improving accuracy by over 4 points compared to previous work. We explore whether manipulating the source text by adding macrolanguage flag tokens or pre-romanizing source strings can improve performance and find that neither manipulation has a positive effect. Finally, we analyze performance differences between the LSTM and Transformer encoders when using a Transformer decoder and find that the Transformer encoder is better able to handle insertions and substitutions when transliterating.",
}
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%0 Conference Proceedings
%T Effective Architectures for Low Resource Multilingual Named Entity Transliteration
%A Moran, Molly
%A Lignos, Constantine
%S Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages
%D 2020
%8 dec
%I Association for Computational Linguistics
%C Suzhou, China
%F moran-lignos-2020-effective
%X In this paper, we evaluate LSTM, biLSTM, GRU, and Transformer architectures for the task of name transliteration in a many-to-one multilingual paradigm, transliterating from 590 languages to English. We experiment with different encoder-decoder combinations and evaluate them using accuracy, character error rate, and an F-measure based on longest continuous subsequences. We find that using a Transformer for the encoder and decoder performs best, improving accuracy by over 4 points compared to previous work. We explore whether manipulating the source text by adding macrolanguage flag tokens or pre-romanizing source strings can improve performance and find that neither manipulation has a positive effect. Finally, we analyze performance differences between the LSTM and Transformer encoders when using a Transformer decoder and find that the Transformer encoder is better able to handle insertions and substitutions when transliterating.
%U https://aclanthology.org/2020.loresmt-1.11
%P 79-86
Markdown (Informal)
[Effective Architectures for Low Resource Multilingual Named Entity Transliteration](https://aclanthology.org/2020.loresmt-1.11) (Moran & Lignos, loresmt 2020)
ACL