@inproceedings{richburg-etal-2020-evaluation,
title = "An Evaluation of Subword Segmentation Strategies for Neural Machine Translation of Morphologically Rich Languages",
author = "Richburg, Aquia and
Eskander, Ramy and
Muresan, Smaranda and
Carpuat, Marine",
editor = "Cunha, Rossana and
Shaikh, Samira and
Varis, Erika and
Georgi, Ryan and
Tsai, Alicia and
Anastasopoulos, Antonios and
Chandu, Khyathi Raghavi",
booktitle = "Proceedings of the Fourth Widening Natural Language Processing Workshop",
month = jul,
year = "2020",
address = "Seattle, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.winlp-1.40",
doi = "10.18653/v1/2020.winlp-1.40",
pages = "151--155",
abstract = "Byte-Pair Encoding (BPE) (Sennrich et al., 2016) has become a standard pre-processing step when building neural machine translation systems. However, it is not clear whether this is an optimal strategy in all settings. We conduct a controlled comparison of subword segmentation strategies for translating two low-resource morphologically rich languages (Swahili and Turkish) into English. We show that segmentations based on a unigram language model (Kudo, 2018) yield comparable BLEU and better recall for translating rare source words than BPE.",
}
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%0 Conference Proceedings
%T An Evaluation of Subword Segmentation Strategies for Neural Machine Translation of Morphologically Rich Languages
%A Richburg, Aquia
%A Eskander, Ramy
%A Muresan, Smaranda
%A Carpuat, Marine
%Y Cunha, Rossana
%Y Shaikh, Samira
%Y Varis, Erika
%Y Georgi, Ryan
%Y Tsai, Alicia
%Y Anastasopoulos, Antonios
%Y Chandu, Khyathi Raghavi
%S Proceedings of the Fourth Widening Natural Language Processing Workshop
%D 2020
%8 July
%I Association for Computational Linguistics
%C Seattle, USA
%F richburg-etal-2020-evaluation
%X Byte-Pair Encoding (BPE) (Sennrich et al., 2016) has become a standard pre-processing step when building neural machine translation systems. However, it is not clear whether this is an optimal strategy in all settings. We conduct a controlled comparison of subword segmentation strategies for translating two low-resource morphologically rich languages (Swahili and Turkish) into English. We show that segmentations based on a unigram language model (Kudo, 2018) yield comparable BLEU and better recall for translating rare source words than BPE.
%R 10.18653/v1/2020.winlp-1.40
%U https://aclanthology.org/2020.winlp-1.40
%U https://doi.org/10.18653/v1/2020.winlp-1.40
%P 151-155
Markdown (Informal)
[An Evaluation of Subword Segmentation Strategies for Neural Machine Translation of Morphologically Rich Languages](https://aclanthology.org/2020.winlp-1.40) (Richburg et al., WiNLP 2020)
ACL