@inproceedings{khakhmovich-etal-2020-cross,
    title = "Cross-lingual Named Entity List Search via Transliteration",
    author = "Khakhmovich, Aleksandr  and
      Pavlova, Svetlana  and
      Kirillova, Kira  and
      Arefyev, Nikolay  and
      Savilova, Ekaterina",
    editor = "Calzolari, Nicoletta  and
      B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Blache, Philippe  and
      Choukri, Khalid  and
      Cieri, Christopher  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Isahara, Hitoshi  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, H{\'e}l{\`e}ne  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2020.lrec-1.524/",
    pages = "4247--4255",
    language = "eng",
    ISBN = "979-10-95546-34-4",
    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."
}