{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/design-challenges-in-named-entity","title":"Design Challenges in Named Entity Transliteration","arxiv_id":"1808.02563","date":"2018-08-07","proceeding":"COLING 2018 8","authors":["Yuval Merhav","Stephen Ash"],"abstract":"We analyze some of the fundamental design challenges that impact the\ndevelopment of a multilingual state-of-the-art named entity transliteration\nsystem, including curating bilingual named entity datasets and evaluation of\nmultiple transliteration methods. We empirically evaluate the transliteration\ntask using traditional weighted finite state transducer (WFST) approach against\ntwo neural approaches: the encoder-decoder recurrent neural network method and\nthe recent, non-sequential Transformer method. In order to improve availability\nof bilingual named entity transliteration datasets, we release personal name\nbilingual dictionaries minded from Wikidata for English to Russian, Hebrew,\nArabic and Japanese Katakana. Our code and dictionaries are publicly available.","url_abs":"http://arxiv.org/abs/1808.02563v1","url_pdf":"http://arxiv.org/pdf/1808.02563v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"design-challenges-in-named-entity","repo_url":"https://github.com/steveash/NETransliteration-COLING2018","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"transliteration","task_name":"Transliteration"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}