Cross-lingual named entity list search via transliteration

Авторы: Khakhmovich Aleksandr, Pavlova Svetlana, Kirillova Kira, Arefyev Nikolay, Savilova Ekaterina
Сборник: LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings
Год издания: 2020
Место издания: European Language Resources Association Marseille, France
Первая страница: 4247
Последняя страница: 4255
Аннотация: 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.
Добавил в систему: Арефьев Николай Викторович

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