Known Words Will Do: Unknown Concept Translation via Lexical Relations

Winston Wu; David Yarowsky

Known Words Will Do: Unknown Concept Translation via Lexical Relations

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Abstract

Translating into low-resource languages is challenging due to the scarcity of training data. In this paper, we propose a probabilistic lexical translation method that bridges through lexical relations including synonyms, hypernyms, hyponyms, and co-hyponyms. This method, which only requires a dictionary like Wiktionary and a lexical database like WordNet, enables the translation of unknown vocabulary into low-resource languages for which we may only know the translation of a related concept. Experiments on translating a core vocabulary set into 472 languages, most of them low-resource, show the effectiveness of our approach.

Anthology ID:: 2022.loresmt-1.3
Volume:: Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022)
Month:: October
Year:: 2022
Address:: Gyeongju, Republic of Korea
Editors:: Atul Kr. Ojha, Chao-Hong Liu, Ekaterina Vylomova, Jade Abbott, Jonathan Washington, Nathaniel Oco, Tommi A Pirinen, Valentin Malykh, Varvara Logacheva, Xiaobing Zhao
Venue:: LoResMT
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 15–22
Language:
URL:: https://aclanthology.org/2022.loresmt-1.3/
DOI:
Bibkey:
Cite (ACL):: Winston Wu and David Yarowsky. 2022. Known Words Will Do: Unknown Concept Translation via Lexical Relations. In Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022), pages 15–22, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):: Known Words Will Do: Unknown Concept Translation via Lexical Relations (Wu & Yarowsky, LoResMT 2022)
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PDF:: https://aclanthology.org/2022.loresmt-1.3.pdf

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@inproceedings{wu-yarowsky-2022-known,
    title = "Known Words Will Do: Unknown Concept Translation via Lexical Relations",
    author = "Wu, Winston  and
      Yarowsky, David",
    editor = "Ojha, Atul Kr.  and
      Liu, Chao-Hong  and
      Vylomova, Ekaterina  and
      Abbott, Jade  and
      Washington, Jonathan  and
      Oco, Nathaniel  and
      Pirinen, Tommi A  and
      Malykh, Valentin  and
      Logacheva, Varvara  and
      Zhao, Xiaobing",
    booktitle = "Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022)",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.loresmt-1.3/",
    pages = "15--22",
    abstract = "Translating into low-resource languages is challenging due to the scarcity of training data. In this paper, we propose a probabilistic lexical translation method that bridges through lexical relations including synonyms, hypernyms, hyponyms, and co-hyponyms. This method, which only requires a dictionary like Wiktionary and a lexical database like WordNet, enables the translation of unknown vocabulary into low-resource languages for which we may only know the translation of a related concept. Experiments on translating a core vocabulary set into 472 languages, most of them low-resource, show the effectiveness of our approach."
}

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%0 Conference Proceedings
%T Known Words Will Do: Unknown Concept Translation via Lexical Relations
%A Wu, Winston
%A Yarowsky, David
%Y Ojha, Atul Kr.
%Y Liu, Chao-Hong
%Y Vylomova, Ekaterina
%Y Abbott, Jade
%Y Washington, Jonathan
%Y Oco, Nathaniel
%Y Pirinen, Tommi A.
%Y Malykh, Valentin
%Y Logacheva, Varvara
%Y Zhao, Xiaobing
%S Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022)
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F wu-yarowsky-2022-known
%X Translating into low-resource languages is challenging due to the scarcity of training data. In this paper, we propose a probabilistic lexical translation method that bridges through lexical relations including synonyms, hypernyms, hyponyms, and co-hyponyms. This method, which only requires a dictionary like Wiktionary and a lexical database like WordNet, enables the translation of unknown vocabulary into low-resource languages for which we may only know the translation of a related concept. Experiments on translating a core vocabulary set into 472 languages, most of them low-resource, show the effectiveness of our approach.
%U https://aclanthology.org/2022.loresmt-1.3/
%P 15-22

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Markdown (Informal)

[Known Words Will Do: Unknown Concept Translation via Lexical Relations](https://aclanthology.org/2022.loresmt-1.3/) (Wu & Yarowsky, LoResMT 2022)

Known Words Will Do: Unknown Concept Translation via Lexical Relations (Wu & Yarowsky, LoResMT 2022)

ACL

Winston Wu and David Yarowsky. 2022. Known Words Will Do: Unknown Concept Translation via Lexical Relations. In Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022), pages 15–22, Gyeongju, Republic of Korea. Association for Computational Linguistics.