Cross-lingual Named Entity List Search via Transliteration

Aleksandr Khakhmovich, Svetlana Pavlova, Kira Kirillova, Nikolay Arefyev, Ekaterina Savilova


Abstract
Out-of-vocabulary words are still a challenge in cross-lingual Natural Language Processing tasks, for which transliteration from source to target language or script is one of the solutions. In this study, we collect a personal name dataset in 445 Wikidata languages (37 scripts), train Transformer-based multilingual transliteration models on 6 high- and 4 less-resourced languages, compare them with bilingual models from (Merhav and Ash, 2018) and determine that multilingual models perform better for less-resourced languages. We discover that intrinsic evaluation, i.e comparison to a single gold standard, might not be appropriate in the task of transliteration due to its high variability. For this reason, we propose using extrinsic evaluation of transliteration via the cross-lingual named entity list search task (e.g. personal name search in contacts list). Our code and datasets are publicly available online.
Anthology ID:
2020.lrec-1.524
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
COLING | LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
4247–4255
Language:
English
URL:
https://www.aclweb.org/anthology/2020.lrec-1.524
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.lrec-1.524.pdf