@inproceedings{dhar-etal-2021-optimal,
title = "Optimal Word Segmentation for Neural Machine Translation into {D}ravidian Languages",
author = "Dhar, Prajit and
Bisazza, Arianna and
van Noord, Gertjan",
booktitle = "Proceedings of the 8th Workshop on Asian Translation (WAT2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wat-1.21",
doi = "10.18653/v1/2021.wat-1.21",
pages = "181--190",
abstract = "Dravidian languages, such as Kannada and Tamil, are notoriously difficult to translate by state-of-the-art neural models. This stems from the fact that these languages are morphologically very rich as well as being low-resourced. In this paper, we focus on subword segmentation and evaluate Linguistically Motivated Vocabulary Reduction (LMVR) against the more commonly used SentencePiece (SP) for the task of translating from English into four different Dravidian languages. Additionally we investigate the optimal subword vocabulary size for each language. We find that SP is the overall best choice for segmentation, and that larger dictionary sizes lead to higher translation quality.",
}
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<abstract>Dravidian languages, such as Kannada and Tamil, are notoriously difficult to translate by state-of-the-art neural models. This stems from the fact that these languages are morphologically very rich as well as being low-resourced. In this paper, we focus on subword segmentation and evaluate Linguistically Motivated Vocabulary Reduction (LMVR) against the more commonly used SentencePiece (SP) for the task of translating from English into four different Dravidian languages. Additionally we investigate the optimal subword vocabulary size for each language. We find that SP is the overall best choice for segmentation, and that larger dictionary sizes lead to higher translation quality.</abstract>
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%0 Conference Proceedings
%T Optimal Word Segmentation for Neural Machine Translation into Dravidian Languages
%A Dhar, Prajit
%A Bisazza, Arianna
%A van Noord, Gertjan
%S Proceedings of the 8th Workshop on Asian Translation (WAT2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F dhar-etal-2021-optimal
%X Dravidian languages, such as Kannada and Tamil, are notoriously difficult to translate by state-of-the-art neural models. This stems from the fact that these languages are morphologically very rich as well as being low-resourced. In this paper, we focus on subword segmentation and evaluate Linguistically Motivated Vocabulary Reduction (LMVR) against the more commonly used SentencePiece (SP) for the task of translating from English into four different Dravidian languages. Additionally we investigate the optimal subword vocabulary size for each language. We find that SP is the overall best choice for segmentation, and that larger dictionary sizes lead to higher translation quality.
%R 10.18653/v1/2021.wat-1.21
%U https://aclanthology.org/2021.wat-1.21
%U https://doi.org/10.18653/v1/2021.wat-1.21
%P 181-190
Markdown (Informal)
[Optimal Word Segmentation for Neural Machine Translation into Dravidian Languages](https://aclanthology.org/2021.wat-1.21) (Dhar et al., WAT 2021)
ACL