[1907.11158] Cross-Lingual Transfer for Distantly Supervised and Low-resources Indonesian NER Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computation and Language arXiv:1907.11158 (cs) [Submitted on 25 Jul 2019] Title: Cross-Lingual Transfer for Distantly Supervised and Low-resources Indonesian NER Authors: Fariz Ikhwantri View a PDF of the paper titled Cross-Lingual Transfer for Distantly Supervised and Low-resources Indonesian NER, by Fariz Ikhwantri View PDF HTML (experimental) Abstract: Manually annotated corpora for low-resource languages are usually small in quantity (gold), or large but distantly supervised (silver). Inspired by recent progress of injecting pre-trained language model (LM) on many Natural Language Processing (NLP) task, we proposed to fine-tune pre-trained language model from high-resources languages to low-resources languages to improve the performance of both scenarios. Our empirical experiment demonstrates significant improvement when fine-tuning pre-trained language model in cross-lingual transfer scenarios for small gold corpus and competitive results in large silver compare to supervised cross-lingual transfer, which will be useful when there is no parallel annotation in the same task to begin. We compare our proposed method of cross-lingual transfer using pre-trained LM to different sources of transfer such as mono-lingual LM and Part-of-Speech tagging (POS) in the downstream task of both large silver and small gold NER dataset by exploiting character-level input of bi-directional language model task. Subjects: Computation and Language (cs.CL) Cite as: arXiv:1907.11158 [cs.CL] (or arXiv:1907.11158v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.1907.11158 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1007/978-3-031-24337-0_29 Focus to learn more DOI(s) linking to related resources Submission history From: Fariz Ikhwantri [ view email ] [v1] Thu, 25 Jul 2019 16:04:09 UTC (3,810 KB) Full-text links: Access Paper: View a PDF of the paper titled Cross-Lingual Transfer for Distantly Supervised and Low-resources Indonesian NER, by Fariz Ikhwantri View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2019-07 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar DBLP - CS Bibliography listing | bibtex Fariz Ikhwantri export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from